diff --git a/main/input/input.txt b/main/input/input.txt index 4ef8c26..883fe8d 100644 --- a/main/input/input.txt +++ b/main/input/input.txt @@ -1,5 +1,43 @@ -Max 2x1 + 3x2 -2x1 + x2 <= 10 -x1 + 3x2 <= 5 +MAX 1 x1 + 7 x2 + 7 x3 + 8 x4 + 4 x5 + 6 x6 + 1 x7 + 10 x8 + 9 x9 + 8 x10 + +6 x1 + 2 x2 + 6 x3 + 7 x4 + 0 x5 + 2 x6 + 2 x7 + 5 x8 + 5 x9 + 10 x10 = 5 +0 x1 + 9 x2 + 10 x3 + 9 x4 + 3 x5 + 3 x6 + 4 x7 + 6 x8 + 6 x9 + 8 x10 <= 60 +1 x1 + 4 x2 + 1 x3 + 8 x4 + 6 x5 + 0 x6 + 6 x7 + 8 x8 + 4 x9 + 4 x10 <= 33 +3 x1 + 5 x2 + 3 x3 + 2 x4 + 10 x5 + 4 x6 + 0 x7 + 1 x8 + 7 x9 + 7 x10 <= 57 +7 x1 + 2 x2 + 3 x3 + 2 x4 + 9 x5 + 10 x6 + 6 x7 + 6 x8 + 9 x9 + 1 x10 <= 64 +6 x1 + 10 x2 + 8 x3 + 5 x4 + 9 x5 + 4 x6 + 8 x7 + 2 x8 + 9 x9 + 5 x10 <= 67 +7 x1 + 2 x2 + 10 x3 + 9 x4 + 3 x5 + 5 x6 + 8 x7 + 6 x8 + 4 x9 + 6 x10 <= 35 +8 x1 + 7 x2 + 4 x3 + 0 x4 + 5 x5 + 7 x6 + 0 x7 + 5 x8 + 4 x9 + 4 x10 <= 42 +4 x1 + 0 x2 + 3 x3 + 3 x4 + 1 x5 + 6 x6 + 6 x7 + 2 x8 + 6 x9 + 7 x10 <= 22 +2 x1 + 10 x2 + 6 x3 + 6 x4 + 4 x5 + 4 x6 + 4 x7 + 9 x8 + 3 x9 + 10 x10 <= 74 +1 x1 + 8 x2 + 10 x3 + 1 x4 + 5 x5 + 10 x6 + 7 x7 + 10 x8 + 8 x9 + 10 x10 <= 49 +4 x1 + 2 x2 + 0 x3 + 8 x4 + 1 x5 + 9 x6 + 4 x7 + 10 x8 + 0 x9 + 10 x10 <= 89 +0 x1 + 5 x2 + 7 x3 + 2 x4 + 10 x5 + 3 x6 + 5 x7 + 2 x8 + 6 x9 + 5 x10 <= 73 +10 x1 + 8 x2 + 2 x3 + 3 x4 + 0 x5 + 6 x6 + 7 x7 + 7 x8 + 8 x9 + 3 x10 <= 64 +4 x1 + 10 x2 + 10 x3 + 4 x4 + 9 x5 + 6 x6 + 4 x7 + 3 x8 + 1 x9 + 5 x10 <= 36 +3 x1 + 9 x2 + 2 x3 + 0 x4 + 7 x5 + 5 x6 + 1 x7 + 8 x8 + 4 x9 + 8 x10 <= 44 +2 x1 + 7 x2 + 8 x3 + 3 x4 + 2 x5 + 5 x6 + 9 x7 + 3 x8 + 3 x9 + 6 x10 <= 29 +1 x1 + 7 x2 + 9 x3 + 6 x4 + 9 x5 + 0 x6 + 3 x7 + 7 x8 + 5 x9 + 9 x10 <= 28 +8 x1 + 8 x2 + 7 x3 + 9 x4 + 3 x5 + 3 x6 + 7 x7 + 1 x8 + 2 x9 + 9 x10 <= 87 +3 x1 + 8 x2 + 5 x3 + 6 x4 + 9 x5 + 2 x6 + 0 x7 + 3 x8 + 10 x9 + 0 x10 <= 100 +9 x1 + 8 x2 + 6 x3 + 10 x4 + 5 x5 + 10 x6 + 7 x7 + 2 x8 + 6 x9 + 2 x10 <= 43 +1 x1 + 9 x2 + 0 x3 + 10 x4 + 3 x5 + 6 x6 + 5 x7 + 6 x8 + 9 x9 + 4 x10 <= 78 +7 x1 + 3 x2 + 6 x3 + 7 x4 + 4 x5 + 10 x6 + 5 x7 + 1 x8 + 3 x9 + 8 x10 <= 87 +3 x1 + 0 x2 + 9 x3 + 5 x4 + 0 x5 + 10 x6 + 6 x7 + 10 x8 + 6 x9 + 10 x10 <= 7 +9 x1 + 4 x2 + 5 x3 + 10 x4 + 7 x5 + 7 x6 + 9 x7 + 5 x8 + 0 x9 + 5 x10 <= 97 +4 x1 + 7 x2 + 7 x3 + 2 x4 + 0 x5 + 1 x6 + 6 x7 + 0 x8 + 10 x9 + 6 x10 <= 68 +3 x1 + 0 x2 + 2 x3 + 5 x4 + 5 x5 + 7 x6 + 10 x7 + 5 x8 + 5 x9 + 10 x10 <= 54 +4 x1 + 10 x2 + 7 x3 + 9 x4 + 10 x5 + 4 x6 + 5 x7 + 6 x8 + 7 x9 + 9 x10 <= 48 +4 x1 + 0 x2 + 8 x3 + 3 x4 + 0 x5 + 7 x6 + 4 x7 + 5 x8 + 10 x9 + 5 x10 <= 44 +3 x1 + 4 x2 + 2 x3 + 7 x4 + 5 x5 + 3 x6 + 5 x7 + 3 x8 + 3 x9 + 2 x10 >= 38 + x1 >= 0 -x2 >= 0 \ No newline at end of file +x2 <= 0 +x3 <= 0 +x4 >= 0 +x5 >= 0 +x6 >= 0 +x7 >= 0 +x8 livre +x9 >= 0 +x10 >= 0 diff --git a/main/main.py b/main/main.py index b13b497..8a35b11 100644 --- a/main/main.py +++ b/main/main.py @@ -1,11 +1,50 @@ +# main.py from parser import Parser -from tableau import Tableau +from simplex import Simplex def main(): filepath = "input/input.txt" - parser = Parser(filepath) result = parser.parse() + print("="*80) + print(f"Tipo: {result['objective_type']}") + print(f"Função Objetivo: {result['objective_coeffs']}") + print(f"Número de variáveis: {result['num_vars']}") + print(f"Restrições: {result['constraints']}") + print("="*80 + "\n") + + simplex = Simplex( + objective_coeffs=result['objective_coeffs'], + constraints=result['constraints'], + objective_type=result['objective_type'], + var_signs=result['var_signs'] + ) + + solution = simplex.solve() + + simplex.write_report("output/resultado.txt") + + print() + print("="*80) + print("RESULTADO") + print("="*80) + print() + print(f"Status: {solution['status']}") + if solution['status'] == 'optimal': + print(f"Valor ótimo: {solution['optimal_value']:.4f}") + print("Solução ótima:") + for i, val in enumerate(solution['solution'], start=1): + print(f" x{i} = {val:.4f}") + elif solution['status'] == 'unbounded': + print("O problema é ILIMITADO (unbounded)") + elif solution['status'] == 'infeasible': + print("O problema é INVIÁVEL (infeasible)") + else: + print(f"Status desconhecido: {solution['status']}") + + print("="*80) + -main() \ No newline at end of file +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/main/output/resultado.txt b/main/output/resultado.txt new file mode 100644 index 0000000..1d60951 --- /dev/null +++ b/main/output/resultado.txt @@ -0,0 +1,421 @@ +Iterações do Simplex +================================================================================ +=== Iteracao: 0 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + a1 6.00 -2.00 -6.00 7.00 0.00 2.00 2.00 5.00 -5.00 5.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 5.00 + w1 0.00 -9.00 -10.00 9.00 3.00 3.00 4.00 6.00 -6.00 6.00 8.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 + w2 1.00 -4.00 -1.00 8.00 6.00 0.00 6.00 8.00 -8.00 4.00 4.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 33.00 + w3 3.00 -5.00 -3.00 2.00 10.00 4.00 0.00 1.00 -1.00 7.00 7.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 57.00 + w4 7.00 -2.00 -3.00 2.00 9.00 10.00 6.00 6.00 -6.00 9.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 64.00 + w5 6.00 -10.00 -8.00 5.00 9.00 4.00 8.00 2.00 -2.00 9.00 5.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 67.00 + w6 7.00 -2.00 -10.00 9.00 3.00 5.00 8.00 6.00 -6.00 4.00 6.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 35.00 + w7 8.00 -7.00 -4.00 0.00 5.00 7.00 0.00 5.00 -5.00 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 42.00 + w8 4.00 -0.00 -3.00 3.00 1.00 6.00 6.00 2.00 -2.00 6.00 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 22.00 + w9 2.00 -10.00 -6.00 6.00 4.00 4.00 4.00 9.00 -9.00 3.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 74.00 + w10 1.00 -8.00 -10.00 1.00 5.00 10.00 7.00 10.00 -10.00 8.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 49.00 + w11 4.00 -2.00 -0.00 8.00 1.00 9.00 4.00 10.00 -10.00 0.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 89.00 + w12 0.00 -5.00 -7.00 2.00 10.00 3.00 5.00 2.00 -2.00 6.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 73.00 + w13 10.00 -8.00 -2.00 3.00 0.00 6.00 7.00 7.00 -7.00 8.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 64.00 + w14 4.00 -10.00 -10.00 4.00 9.00 6.00 4.00 3.00 -3.00 1.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 36.00 + w15 3.00 -9.00 -2.00 0.00 7.00 5.00 1.00 8.00 -8.00 4.00 8.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.00 + w16 2.00 -7.00 -8.00 3.00 2.00 5.00 9.00 3.00 -3.00 3.00 6.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 29.00 + w17 1.00 -7.00 -9.00 6.00 9.00 0.00 3.00 7.00 -7.00 5.00 9.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 28.00 + w18 8.00 -8.00 -7.00 9.00 3.00 3.00 7.00 1.00 -1.00 2.00 9.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 87.00 + w19 3.00 -8.00 -5.00 6.00 9.00 2.00 0.00 3.00 -3.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 + w20 9.00 -8.00 -6.00 10.00 5.00 10.00 7.00 2.00 -2.00 6.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 43.00 + w21 1.00 -9.00 -0.00 10.00 3.00 6.00 5.00 6.00 -6.00 9.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 78.00 + w22 7.00 -3.00 -6.00 7.00 4.00 10.00 5.00 1.00 -1.00 3.00 8.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 87.00 + w23 3.00 -0.00 -9.00 5.00 0.00 10.00 6.00 10.00 -10.00 6.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.00 + w24 9.00 -4.00 -5.00 10.00 7.00 7.00 9.00 5.00 -5.00 0.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 97.00 + w25 4.00 -7.00 -7.00 2.00 0.00 1.00 6.00 0.00 -0.00 10.00 6.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 68.00 + w26 3.00 -0.00 -2.00 5.00 5.00 7.00 10.00 5.00 -5.00 5.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 54.00 + w27 4.00 -10.00 -7.00 9.00 10.00 4.00 5.00 6.00 -6.00 7.00 9.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 48.00 + w28 4.00 -0.00 -8.00 3.00 0.00 7.00 4.00 5.00 -5.00 10.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 44.00 + a2 3.00 -4.00 -2.00 7.00 5.00 3.00 5.00 3.00 -3.00 3.00 2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 1.00 38.00 + Z-9000001.006000007.008000007.00-14000008.00-5000004.00-5000006.00-7000001.00-8000010.008000010.00-8000009.00-12000008.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001000000.00 0.00 0.00-43000000.00 + +=== Iteracao: 1 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.86 -0.29 -0.86 1.00 0.00 0.29 0.29 0.71 -0.71 0.71 1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.71 + w1 -7.71 -6.43 -2.29 0.00 3.00 0.43 1.43 -0.43 0.43 -0.43 -4.86 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.29 0.00 53.57 + w2 -5.86 -1.71 5.86 0.00 6.00 -2.29 3.71 2.29 -2.29 -1.71 -7.43 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.14 0.00 27.29 + w3 1.29 -4.43 -1.29 0.00 10.00 3.43 -0.57 -0.43 0.43 5.57 4.14 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.29 0.00 55.57 + w4 5.29 -1.43 -1.29 0.00 9.00 9.43 5.43 4.57 -4.57 7.57 -1.86 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.29 0.00 62.57 + w5 1.71 -8.57 -3.71 0.00 9.00 2.57 6.57 -1.57 1.57 5.43 -2.14 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.71 0.00 63.43 + w6 -0.71 0.57 -2.29 0.00 3.00 2.43 5.43 -0.43 0.43 -2.43 -6.86 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.29 0.00 28.57 + w7 8.00 -7.00 -4.00 0.00 5.00 7.00 0.00 5.00 -5.00 4.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 42.00 + w8 1.43 0.86 -0.43 0.00 1.00 5.14 5.14 -0.14 0.14 3.86 2.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.43 0.00 19.86 + w9 -3.14 -8.29 -0.86 0.00 4.00 2.29 2.29 4.71 -4.71 -1.29 1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 69.71 + w10 0.14 -7.71 -9.14 0.00 5.00 9.71 6.71 9.29 -9.29 7.29 8.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.14 0.00 48.29 + w11 -2.86 0.29 6.86 0.00 1.00 6.71 1.71 4.29 -4.29 -5.71 -1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.14 0.00 83.29 + w12 -1.71 -4.43 -5.29 0.00 10.00 2.43 4.43 0.57 -0.57 4.57 2.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.29 0.00 71.57 + w13 7.43 -7.14 0.57 0.00 0.00 5.14 6.14 4.86 -4.86 5.86 -1.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.43 0.00 61.86 + w14 0.57 -8.86 -6.57 0.00 9.00 4.86 2.86 0.14 -0.14 -1.86 -0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.57 0.00 33.14 + w15 3.00 -9.00 -2.00 0.00 7.00 5.00 1.00 8.00 -8.00 4.00 8.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.00 + w16 -0.57 -6.14 -5.43 0.00 2.00 4.14 8.14 0.86 -0.86 0.86 1.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.43 0.00 26.86 + w17 -4.14 -5.29 -3.86 0.00 9.00 -1.71 1.29 2.71 -2.71 0.71 0.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 23.71 + w18 0.29 -5.43 0.71 0.00 3.00 0.43 4.43 -5.43 5.43 -4.43 -3.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.29 0.00 80.57 + w19 -2.14 -6.29 0.14 0.00 9.00 0.29 -1.71 -1.29 1.29 5.71 -8.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 95.71 + w20 0.43 -5.14 2.57 0.00 5.00 7.14 4.14 -5.14 5.14 -1.14 -12.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.43 0.00 35.86 + w21 -7.57 -6.14 8.57 0.00 3.00 3.14 2.14 -1.14 1.14 1.86 -10.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.43 0.00 70.86 + w22 1.00 -1.00 0.00 0.00 4.00 8.00 3.00 -4.00 4.00 -2.00 -2.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 82.00 + w23 -1.29 1.43 -4.71 0.00 0.00 8.57 4.57 6.43 -6.43 2.43 2.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.71 0.00 3.43 + w24 0.43 -1.14 3.57 0.00 7.00 4.14 6.14 -2.14 2.14 -7.14 -9.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -1.43 0.00 89.86 + w25 2.29 -6.43 -5.29 0.00 0.00 0.43 5.43 -1.43 1.43 8.57 3.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -0.29 0.00 66.57 + w26 -1.29 1.43 2.29 0.00 5.00 5.57 8.57 1.43 -1.43 1.43 2.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -0.71 0.00 50.43 + w27 -3.71 -7.43 0.71 0.00 10.00 1.43 2.43 -0.43 0.43 0.57 -3.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.29 0.00 41.57 + w28 1.43 0.86 -5.43 0.00 0.00 6.14 3.14 2.86 -2.86 7.86 0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.43 0.00 41.86 + a2 -3.00 -2.00 4.00 0.00 5.00 1.00 3.00 -2.00 2.00 -2.00 -8.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 -1.00 1.00 33.00 + Z3000005.862000004.71-3999999.86 0.00-5000004.00-1000003.71-2999998.711999995.71-1999995.711999996.718000003.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001000000.002000001.14 0.00-32999994.29 + +=== Iteracao: 2 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.86 -0.29 -0.86 1.00 0.00 0.29 0.29 0.71 -0.71 0.71 1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.71 + w1 -6.33 -4.67 -1.00 0.00 0.00 1.00 1.00 -1.33 1.33 -0.67 -5.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 45.67 + w2 -3.10 1.81 8.43 0.00 0.00 -1.14 2.86 0.48 -0.48 -2.19 -7.71 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.57 0.00 11.48 + w3 5.89 1.44 3.00 0.00 0.00 5.33 -2.00 -3.44 3.44 4.78 3.67 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 29.22 + w4 9.43 3.86 2.57 0.00 0.00 11.14 4.14 1.86 -1.86 6.86 -2.29 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.00 38.86 + w5 5.86 -3.29 0.14 0.00 0.00 4.29 5.29 -4.29 4.29 4.71 -2.57 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 39.71 + w6 0.67 2.33 -1.00 0.00 0.00 3.00 5.00 -1.33 1.33 -2.67 -7.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 20.67 + w7 10.30 -4.06 -1.86 0.00 0.00 7.95 -0.71 3.49 -3.49 3.60 3.76 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.48 0.00 28.83 + w8 1.89 1.44 -0.00 0.00 0.00 5.33 5.00 -0.44 0.44 3.78 2.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 17.22 + w9 -1.30 -5.94 0.86 0.00 0.00 3.05 1.71 3.51 -3.51 -1.60 1.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.48 0.00 59.17 + w10 2.44 -4.78 -7.00 0.00 0.00 10.67 6.00 7.78 -7.78 6.89 8.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 35.11 + w11 -2.40 0.87 7.29 0.00 0.00 6.90 1.57 3.98 -3.98 -5.79 -1.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.05 0.00 80.65 + w12 2.89 1.44 -1.00 0.00 0.00 4.33 3.00 -2.44 2.44 3.78 1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 45.22 + w13 7.43 -7.14 0.57 0.00 0.00 5.14 6.14 4.86 -4.86 5.86 -1.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.43 0.00 61.86 + w14 4.71 -3.57 -2.71 0.00 0.00 6.57 1.57 -2.57 2.57 -2.57 -1.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.00 9.43 + w15 6.22 -4.89 1.00 0.00 0.00 6.33 -0.00 5.89 -5.89 3.44 7.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 25.56 + w16 0.35 -4.97 -4.57 0.00 0.00 4.52 7.86 0.25 -0.25 0.70 1.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.24 0.00 21.59 + x5 -0.46 -0.59 -0.43 0.00 1.00 -0.19 0.14 0.30 -0.30 0.08 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.10 0.00 2.63 + w18 1.67 -3.67 2.00 0.00 0.00 1.00 4.00 -6.33 6.33 -4.67 -4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 72.67 + w19 2.00 -1.00 4.00 0.00 0.00 2.00 -3.00 -4.00 4.00 5.00 -9.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 72.00 + w20 2.73 -2.21 4.71 0.00 0.00 8.10 3.43 -6.65 6.65 -1.54 -12.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.56 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.95 0.00 22.68 + w21 -6.19 -4.38 9.86 0.00 0.00 3.71 1.71 -2.05 2.05 1.62 -10.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.14 0.00 62.95 + w22 2.84 1.35 1.71 0.00 0.00 8.76 2.43 -5.21 5.21 -2.32 -2.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.44 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.62 0.00 71.46 + w23 -1.29 1.43 -4.71 0.00 0.00 8.57 4.57 6.43 -6.43 2.43 2.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.71 0.00 3.43 + w24 3.65 2.97 6.57 0.00 0.00 5.48 5.14 -4.25 4.25 -7.70 -9.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.78 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.76 0.00 71.41 + w25 2.29 -6.43 -5.29 0.00 0.00 0.43 5.43 -1.43 1.43 8.57 3.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -0.29 0.00 66.57 + w26 1.02 4.37 4.43 0.00 0.00 6.52 7.86 -0.08 0.08 1.03 2.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -0.24 0.00 37.25 + w27 0.89 -1.56 5.00 0.00 0.00 3.33 1.00 -3.44 3.44 -0.22 -4.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.33 0.00 15.22 + w28 1.43 0.86 -5.43 0.00 0.00 6.14 3.14 2.86 -2.86 7.86 0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.43 0.00 41.86 + a2 -0.70 0.94 6.14 0.00 0.00 1.95 2.29 -3.51 3.51 -2.40 -8.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 -0.52 1.00 19.83 + Z 698416.71-936505.57-6142858.71 0.00 0.00-1952385.43-2285712.433507933.43-3507933.432396822.438238098.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 555556.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001000000.001523810.29 0.00-19825380.57 + +=== Iteracao: 3 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.54 -0.10 0.00 1.00 0.00 0.17 0.58 0.76 -0.76 0.49 0.64 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 1.88 + w1 -6.70 -4.45 0.00 0.00 0.00 0.86 1.34 -1.28 1.28 -0.93 -5.92 1.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.07 0.00 47.03 + x3 -0.37 0.21 1.00 0.00 0.00 -0.14 0.34 0.06 -0.06 -0.26 -0.92 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 1.36 + w3 6.99 0.80 0.00 0.00 0.00 5.74 -3.02 -3.61 3.61 5.56 6.41 0.00 -0.36 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.87 0.00 25.14 + w4 10.37 3.31 0.00 0.00 0.00 11.49 3.27 1.71 -1.71 7.53 0.07 0.00 -0.31 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 0.00 35.36 + w5 5.91 -3.32 0.00 0.00 0.00 4.31 5.24 -4.29 4.29 4.75 -2.44 0.00 -0.02 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 39.52 + w6 0.30 2.55 0.00 0.00 0.00 2.86 5.34 -1.28 1.28 -2.93 -7.92 0.00 0.12 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.07 0.00 22.03 + w7 9.62 -3.66 0.00 0.00 0.00 7.70 -0.08 3.60 -3.60 3.12 2.06 0.00 0.22 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00 31.35 + w8 1.89 1.44 0.00 0.00 0.00 5.33 5.00 -0.44 0.44 3.78 2.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 17.22 + w9 -0.99 -6.12 0.00 0.00 0.00 3.16 1.42 3.46 -3.46 -1.38 2.02 0.00 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.42 0.00 58.01 + w10 -0.13 -3.27 0.00 0.00 0.00 9.72 8.37 8.17 -8.17 5.07 1.93 0.00 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.14 0.00 44.64 + w11 0.28 -0.69 0.00 0.00 0.00 7.89 -0.90 3.57 -3.57 -3.90 5.19 0.00 -0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.55 0.00 70.73 + w12 2.52 1.66 0.00 0.00 0.00 4.20 3.34 -2.39 2.39 3.52 0.75 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 46.58 + w13 7.64 -7.27 0.00 0.00 0.00 5.22 5.95 4.82 -4.82 6.01 -0.76 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.39 0.00 61.08 + w14 3.72 -2.99 0.00 0.00 0.00 6.20 2.49 -2.42 2.42 -3.28 -3.63 0.00 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 13.12 + w15 6.59 -5.10 0.00 0.00 0.00 6.47 -0.34 5.83 -5.83 3.70 8.58 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 0.00 24.19 + w16 -1.33 -3.99 0.00 0.00 0.00 3.90 9.41 0.51 -0.51 -0.49 -2.56 0.00 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.55 0.00 27.81 + x5 -0.62 -0.50 0.00 0.00 1.00 -0.25 0.29 0.33 -0.33 -0.03 -0.34 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00 3.22 + w18 2.40 -4.10 0.00 0.00 0.00 1.27 3.32 -6.45 6.45 -4.15 -2.17 0.00 -0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.18 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 69.94 + w19 3.47 -1.86 0.00 0.00 0.00 2.54 -4.36 -4.23 4.23 6.04 -5.34 0.00 -0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.68 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 66.55 + w20 4.46 -3.22 0.00 0.00 0.00 8.73 1.83 -6.92 6.92 -0.31 -8.21 0.00 -0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.18 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.63 0.00 16.26 + w21 -2.57 -6.50 0.00 0.00 0.00 5.05 -1.63 -2.60 2.60 4.18 -1.41 0.00 -1.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.47 0.00 49.53 + w22 3.47 0.98 0.00 0.00 0.00 8.99 1.85 -5.30 5.30 -1.87 -0.62 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.31 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.50 0.00 69.13 + w23 -3.02 2.44 0.00 0.00 0.00 7.93 6.17 6.69 -6.69 1.20 -1.46 0.00 0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.37 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.03 0.00 9.85 + w24 6.06 1.56 0.00 0.00 0.00 6.37 2.92 -4.63 4.63 -5.99 -3.60 0.00 -0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.26 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.32 0.00 62.47 + w25 0.34 -5.29 0.00 0.00 0.00 -0.29 7.22 -1.13 1.13 7.20 -1.69 0.00 0.63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -0.64 0.00 73.77 + w26 2.64 3.41 0.00 0.00 0.00 7.12 6.36 -0.33 0.33 2.18 6.67 0.00 -0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.06 0.00 31.22 + w27 2.73 -2.63 0.00 0.00 0.00 4.01 -0.69 -3.73 3.73 1.08 0.24 0.00 -0.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.01 0.00 8.41 + w28 -0.56 2.02 0.00 0.00 0.00 5.41 4.98 3.16 -3.16 6.45 -4.25 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.80 0.00 49.25 + a2 1.56 -0.38 0.00 0.00 0.00 2.79 0.20 -3.85 3.85 -0.80 -2.62 0.00 -0.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.00 -0.11 1.00 11.46 + Z-1557435.36 382300.25 0.00 0.00 0.00-2785315.42-203387.443854987.59-3854987.59 800373.272615821.39 0.00 728813.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 69680.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001000000.001107345.29 0.00-11461375.20 + +=== Iteracao: 4 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 1.10 -0.64 0.00 1.00 0.00 0.99 0.43 0.00 0.00 0.71 0.69 0.00 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.09 0.00 3.60 + w1 -7.63 -3.55 0.00 0.00 0.00 -0.51 1.58 0.00 0.00 -1.30 -6.00 1.00 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.34 0.00 0.00 -1.07 0.00 44.15 + x3 -0.33 0.17 1.00 0.00 0.00 -0.07 0.33 0.00 0.00 -0.24 -0.91 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 -0.07 0.00 1.49 + w3 4.35 3.35 0.00 0.00 0.00 1.85 -2.34 0.00 0.00 4.51 6.18 0.00 0.22 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.97 0.00 0.00 0.86 0.00 16.98 + w4 11.62 2.10 0.00 0.00 0.00 13.33 2.95 0.00 0.00 8.02 0.18 0.00 -0.58 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.46 0.00 0.00 0.75 0.00 39.22 + w5 2.77 -0.29 0.00 0.00 0.00 -0.32 6.04 0.00 0.00 3.51 -2.72 0.00 0.67 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.15 0.00 0.00 0.15 0.00 29.83 + w6 -0.63 3.45 0.00 0.00 0.00 1.49 5.58 0.00 0.00 -3.30 -8.00 0.00 0.32 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.34 0.00 0.00 -1.07 0.00 19.15 + w7 12.25 -6.20 0.00 0.00 0.00 11.57 -0.76 0.00 0.00 4.16 2.30 0.00 -0.35 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.00 0.00 0.36 0.00 39.47 + w8 1.56 1.76 0.00 0.00 0.00 4.85 5.08 0.00 0.00 3.65 2.64 0.00 0.07 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00 0.00 -0.33 0.00 16.22 + w9 1.54 -8.56 0.00 0.00 0.00 6.89 0.78 0.00 0.00 -0.38 2.25 0.00 -0.65 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.93 0.00 0.00 -0.41 0.00 65.82 + w10 5.85 -9.04 0.00 0.00 0.00 18.51 6.85 0.00 0.00 7.43 2.46 0.00 -0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.19 0.00 0.00 -0.13 0.00 63.09 + w11 2.89 -3.21 0.00 0.00 0.00 11.74 -1.56 0.00 0.00 -2.87 5.42 0.00 -1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.96 0.00 0.00 -0.55 0.00 78.80 + w12 0.78 3.34 0.00 0.00 0.00 1.63 3.78 0.00 0.00 2.83 0.60 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -0.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.64 0.00 0.00 0.60 0.00 41.19 + w13 11.17 -10.67 0.00 0.00 0.00 10.41 5.05 0.00 0.00 7.40 -0.45 0.00 -0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -0.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.29 0.00 0.00 -0.38 0.00 71.97 + w14 1.95 -1.28 0.00 0.00 0.00 3.60 2.94 0.00 0.00 -3.98 -3.78 0.00 0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.65 0.00 0.00 0.10 0.00 7.66 + w15 10.85 -9.22 0.00 0.00 0.00 12.75 -1.43 0.00 0.00 5.39 8.96 0.00 -1.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -1.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.56 0.00 0.00 0.74 0.00 37.36 + w16 -0.96 -4.35 0.00 0.00 0.00 4.46 9.31 0.00 0.00 -0.34 -2.53 0.00 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 -0.55 0.00 28.97 + x5 -0.38 -0.73 0.00 0.00 1.00 0.10 0.23 0.00 0.00 0.06 -0.32 0.00 -0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 -0.12 0.00 3.95 + w18 -2.31 0.45 0.00 0.00 0.00 -5.67 4.52 0.00 0.00 -6.01 -2.59 0.00 0.79 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.06 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.73 0.00 0.00 -0.87 0.00 55.39 + w19 0.38 1.12 0.00 0.00 0.00 -2.01 -3.57 0.00 0.00 4.82 -5.61 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.13 0.00 0.00 0.26 0.00 57.01 + w20 -0.60 1.66 0.00 0.00 0.00 1.29 3.12 0.00 0.00 -2.31 -8.66 0.00 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.15 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.86 0.00 0.00 -0.64 0.00 0.65 + w21 -4.47 -4.66 0.00 0.00 0.00 2.25 -1.14 0.00 0.00 3.43 -1.58 0.00 -0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.95 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.70 0.00 0.00 -0.48 0.00 43.65 + w22 -0.41 4.72 0.00 0.00 0.00 3.29 2.84 0.00 0.00 -3.40 -0.97 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.42 0.00 0.00 -0.51 0.00 57.15 + w23 1.88 -2.28 0.00 0.00 0.00 15.14 4.92 0.00 0.00 3.14 -1.02 0.00 -0.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.66 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.80 0.00 0.00 -1.02 0.00 24.96 + w24 2.68 4.82 0.00 0.00 0.00 1.39 3.78 0.00 0.00 -7.33 -3.91 0.00 -0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.63 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.24 0.00 0.00 -0.32 0.00 52.02 + w25 -0.48 -4.50 0.00 0.00 0.00 -1.50 7.43 0.00 0.00 6.87 -1.77 0.00 0.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.30 0.00 0.00 -0.65 0.00 71.22 + w26 2.40 3.65 0.00 0.00 0.00 6.77 6.42 0.00 0.00 2.09 6.65 0.00 -0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.09 0.00 0.00 0.06 0.00 30.48 + x9 0.73 -0.71 0.00 0.00 0.00 1.08 -0.19 -1.00 1.00 0.29 0.07 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.00 0.00 0.00 2.26 + w28 1.75 -0.21 0.00 0.00 0.00 8.81 4.39 0.00 0.00 7.36 -4.05 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.85 1.00 0.00 -0.79 0.00 56.39 + a2 -1.26 2.34 0.00 0.00 0.00 -1.36 0.92 0.00 0.00 -1.91 -2.87 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.03 0.00 -1.00 -0.11 1.00 2.76 + Z1261244.30-2337034.10 0.00 0.00 0.001363812.20-922179.97 0.00 0.001914599.092867106.58 0.00 115210.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-670539.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001034359.99 0.001000000.001113189.13 0.00-2757946.92 + +=== Iteracao: 5 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.87 0.00 0.00 1.00 0.00 1.49 1.64 0.00 0.00 -0.18 -2.64 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.00 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 -0.51 0.00 0.00 -0.16 0.00 3.85 + w1 -8.91 0.00 0.00 0.00 0.00 2.25 8.25 0.00 0.00 -6.24 -24.51 1.00 1.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.28 0.00 0.00 2.14 0.00 0.00 0.00 0.00 0.00 0.00 -4.31 0.00 0.00 -2.45 0.00 45.53 + x3 -0.26 0.00 1.00 0.00 0.00 -0.21 -0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.21 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.00 1.42 + w3 5.55 0.00 0.00 0.00 0.00 -0.75 -8.64 0.00 0.00 9.18 23.64 0.00 -0.87 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.49 0.00 0.00 -2.02 0.00 0.00 0.00 0.00 0.00 0.00 2.77 0.00 0.00 2.16 0.00 15.67 + w4 12.38 0.00 0.00 0.00 0.00 11.71 -0.99 0.00 0.00 10.94 11.12 0.00 -1.26 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.57 0.00 0.00 -1.26 0.00 0.00 0.00 0.00 0.00 0.00 2.80 0.00 0.00 1.56 0.00 38.40 + w5 2.67 0.00 0.00 0.00 0.00 -0.09 6.58 0.00 0.00 3.11 -4.22 0.00 0.76 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.00 -1.47 0.00 0.00 0.03 0.00 29.94 + w6 0.60 0.00 0.00 0.00 0.00 -1.19 -0.90 0.00 0.00 1.51 9.98 0.00 -0.80 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.55 0.00 0.00 -2.08 0.00 0.00 0.00 0.00 0.00 0.00 3.51 0.00 0.00 0.27 0.00 17.80 + w7 10.02 0.00 0.00 0.00 0.00 16.39 10.90 0.00 0.00 -4.48 -30.04 0.00 1.67 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.88 0.00 0.00 3.73 0.00 0.00 0.00 0.00 0.00 0.00 -5.97 0.00 0.00 -2.05 0.00 41.89 + w8 2.20 0.00 0.00 0.00 0.00 3.49 1.78 0.00 0.00 6.10 11.80 0.00 -0.50 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.24 0.00 0.00 -1.06 0.00 0.00 0.00 0.00 0.00 0.00 1.85 0.00 0.00 0.35 0.00 15.53 + w9 -1.53 0.00 0.00 0.00 0.00 13.53 16.86 0.00 0.00 -12.31 -42.39 0.00 2.14 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.86 0.00 0.00 5.15 0.00 0.00 0.00 0.00 0.00 0.00 -8.64 0.00 0.00 -3.73 0.00 69.15 + w10 2.60 0.00 0.00 0.00 0.00 25.53 23.83 0.00 0.00 -5.16 -44.68 0.00 2.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 3.56 0.00 0.00 5.44 0.00 0.00 0.00 0.00 0.00 0.00 -7.91 0.00 0.00 -3.63 0.00 66.62 + w11 1.74 0.00 0.00 0.00 0.00 14.23 4.47 0.00 0.00 -7.34 -11.32 0.00 -0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.99 0.00 0.00 1.93 0.00 0.00 0.00 0.00 0.00 0.00 -2.63 0.00 0.00 -1.79 0.00 80.05 + w12 1.98 0.00 0.00 0.00 0.00 -0.97 -2.50 0.00 0.00 7.49 18.03 0.00 -0.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -3.04 0.00 0.00 -2.01 0.00 0.00 0.00 0.00 0.00 0.00 3.10 0.00 0.00 1.89 0.00 39.89 + w13 7.34 0.00 0.00 0.00 0.00 18.70 25.09 0.00 0.00 -7.46 -56.08 0.00 2.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 6.48 0.00 0.00 6.42 0.00 0.00 0.00 0.00 0.00 0.00 -10.63 0.00 0.00 -4.51 0.00 76.13 + w14 1.49 0.00 0.00 0.00 0.00 4.60 5.35 0.00 0.00 -5.76 -10.47 0.00 1.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.13 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.00 -2.08 0.00 0.00 -0.40 0.00 8.16 + w15 7.54 0.00 0.00 0.00 0.00 19.90 15.89 0.00 0.00 -7.45 -39.10 0.00 1.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 4.54 0.00 0.00 5.55 0.00 0.00 0.00 0.00 0.00 0.00 -8.74 0.00 0.00 -2.83 0.00 40.95 + w16 -2.52 0.00 0.00 0.00 0.00 7.83 17.48 0.00 0.00 -6.40 -25.20 0.00 1.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 2.32 0.00 0.00 2.62 0.00 0.00 0.00 0.00 0.00 0.00 -4.72 0.00 0.00 -2.23 0.00 30.66 + x5 -0.64 0.00 0.00 0.00 1.00 0.67 1.59 0.00 0.00 -0.95 -4.10 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.51 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 -0.72 0.00 0.00 -0.40 0.00 4.24 + w18 -2.15 0.00 0.00 0.00 0.00 -6.02 3.68 0.00 0.00 -5.38 -0.24 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 1.00 0.00 -0.27 0.00 0.00 0.00 0.00 0.00 0.00 -1.23 0.00 0.00 -0.70 0.00 55.21 + w19 0.78 0.00 0.00 0.00 0.00 -2.88 -5.68 0.00 0.00 6.38 0.24 0.00 -0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.65 0.00 1.00 -0.68 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.70 0.00 56.58 + x2 -0.36 1.00 0.00 0.00 0.00 0.78 1.88 0.00 0.00 -1.39 -5.21 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.69 0.00 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.00 -1.12 0.00 0.00 -0.39 0.00 0.39 + w21 -6.15 0.00 0.00 0.00 0.00 5.87 7.61 0.00 0.00 -3.06 -25.87 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.16 0.00 0.00 2.81 1.00 0.00 0.00 0.00 0.00 0.00 -5.91 0.00 0.00 -2.28 0.00 45.47 + w22 1.29 0.00 0.00 0.00 0.00 -0.38 -6.03 0.00 0.00 3.17 23.65 0.00 -0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.55 0.00 0.00 -2.84 0.00 1.00 0.00 0.00 0.00 0.00 3.85 0.00 0.00 1.32 0.00 55.31 + w23 1.06 0.00 0.00 0.00 0.00 16.91 9.21 0.00 0.00 -0.04 -12.92 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.08 0.00 0.00 1.37 0.00 0.00 1.00 0.00 0.00 0.00 -0.75 0.00 0.00 -1.91 0.00 25.85 + w24 4.41 0.00 0.00 0.00 0.00 -2.35 -5.28 0.00 0.00 -0.61 21.23 0.00 -1.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.69 0.00 0.00 -2.90 0.00 0.00 0.00 1.00 0.00 0.00 4.15 0.00 0.00 1.54 0.00 50.15 + w25 -2.10 0.00 0.00 0.00 0.00 1.99 15.88 0.00 0.00 0.61 -25.21 0.00 2.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.90 0.00 0.00 2.71 0.00 0.00 0.00 0.00 1.00 0.00 -5.33 0.00 0.00 -2.39 0.00 72.97 + w26 3.71 0.00 0.00 0.00 0.00 3.94 -0.43 0.00 0.00 7.17 25.66 0.00 -1.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.66 0.00 0.00 -2.20 0.00 0.00 0.00 0.00 0.00 1.00 3.99 0.00 0.00 1.47 0.00 29.06 + x9 0.48 0.00 0.00 0.00 0.00 1.62 1.14 -1.00 1.00 -0.69 -3.61 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00 0.00 -0.52 0.00 0.00 -0.27 0.00 2.53 + w28 1.67 0.00 0.00 0.00 0.00 8.97 4.79 0.00 0.00 7.07 -5.14 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.89 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.62 1.00 0.00 -0.87 0.00 56.47 + a2 -0.42 0.00 0.00 0.00 0.00 -3.18 -3.47 0.00 0.00 1.34 9.32 0.00 -0.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.94 0.00 0.00 -1.41 0.00 0.00 0.00 0.00 0.00 0.00 1.58 0.00 -1.00 0.79 1.00 1.85 + Z 422272.53 0.00 0.00 0.00 0.003178266.423468202.00 0.00 0.00-1341038.00-9317892.00 0.00 877394.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 941281.42 0.00 0.001407055.21 0.00 0.00 0.00 0.00 0.00 0.00-1577117.42 0.001000000.00 208095.00 0.00-1847875.84 + +=== Iteracao: 6 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.75 0.00 0.00 1.00 0.00 0.59 0.65 0.00 0.00 0.20 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.04 0.00 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 -0.28 0.06 0.28 4.38 + w1 -10.02 0.00 0.00 0.00 0.00 -6.11 -0.88 0.00 0.00 -2.71 0.00 1.00 -0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.19 0.00 0.00 -1.56 0.00 0.00 0.00 0.00 0.00 0.00 -0.16 0.00 -2.63 -0.36 2.63 50.39 + x3 -0.26 0.00 1.00 0.00 0.00 -0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.21 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.00 0.00 -0.00 -0.00 1.42 + w3 6.62 0.00 0.00 0.00 0.00 7.31 0.16 0.00 0.00 5.78 0.00 0.00 1.35 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.10 0.00 0.00 1.55 0.00 0.00 0.00 0.00 0.00 0.00 -1.23 0.00 2.54 0.15 -2.54 10.99 + w4 12.88 0.00 0.00 0.00 0.00 15.50 3.15 0.00 0.00 9.34 0.00 0.00 -0.21 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.45 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.92 0.00 1.19 0.62 -1.19 36.20 + w5 2.48 0.00 0.00 0.00 0.00 -1.53 5.01 0.00 0.00 3.72 0.00 0.00 0.36 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.39 0.00 0.00 -0.46 0.00 0.00 0.00 0.00 0.00 0.00 -0.76 0.00 -0.45 0.39 0.45 30.77 + w6 1.06 0.00 0.00 0.00 0.00 2.22 2.81 0.00 0.00 0.07 0.00 0.00 0.14 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.54 0.00 0.00 -0.57 0.00 0.00 0.00 0.00 0.00 0.00 1.82 0.00 1.07 -0.58 -1.07 15.82 + w7 8.66 0.00 0.00 0.00 0.00 6.14 -0.29 0.00 0.00 -0.16 0.00 0.00 -1.16 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.15 0.00 0.00 -0.80 0.00 0.00 0.00 0.00 0.00 0.00 -0.88 0.00 -3.22 0.51 3.22 47.85 + w8 2.73 0.00 0.00 0.00 0.00 7.52 6.17 0.00 0.00 4.40 0.00 0.00 0.61 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.05 0.00 0.00 0.72 0.00 0.00 0.00 0.00 0.00 0.00 -0.15 0.00 1.27 -0.66 -1.27 13.19 + w9 -3.45 0.00 0.00 0.00 0.00 -0.92 1.08 0.00 0.00 -6.21 0.00 0.00 -1.85 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.58 0.00 0.00 -1.25 0.00 0.00 0.00 0.00 0.00 0.00 -1.46 0.00 -4.55 -0.13 4.55 77.56 + w10 0.58 0.00 0.00 0.00 0.00 10.29 7.20 0.00 0.00 1.27 0.00 0.00 -1.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.96 0.00 0.00 -1.30 0.00 0.00 0.00 0.00 0.00 0.00 -0.35 0.00 -4.79 0.17 4.79 75.48 + w11 1.23 0.00 0.00 0.00 0.00 10.37 0.26 0.00 0.00 -5.71 0.00 0.00 -1.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.85 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 -0.71 0.00 -1.21 -0.83 1.21 82.29 + w12 2.79 0.00 0.00 0.00 0.00 5.18 4.21 0.00 0.00 4.89 0.00 0.00 1.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.22 0.00 0.00 0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 1.93 0.36 -1.93 36.32 + w13 4.79 0.00 0.00 0.00 0.00 -0.43 4.22 0.00 0.00 0.61 0.00 0.00 -2.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.81 0.00 0.00 -2.04 0.00 0.00 0.00 0.00 0.00 0.00 -1.14 0.00 -6.02 0.25 6.02 87.25 + w14 1.01 0.00 0.00 0.00 0.00 1.02 1.45 0.00 0.00 -4.26 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.92 0.00 0.00 -0.81 0.00 0.00 0.00 0.00 0.00 0.00 -0.31 0.00 -1.12 0.49 1.12 10.24 + w15 5.77 0.00 0.00 0.00 0.00 6.57 1.34 0.00 0.00 -1.82 0.00 0.00 -1.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.59 0.00 0.00 -0.35 0.00 0.00 0.00 0.00 0.00 0.00 -2.12 0.00 -4.20 0.50 4.20 48.71 + w16 -3.66 0.00 0.00 0.00 0.00 -0.77 8.10 0.00 0.00 -2.77 0.00 0.00 -0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.23 0.00 0.00 -1.19 0.00 0.00 0.00 0.00 0.00 0.00 -0.46 0.00 -2.70 -0.09 2.70 35.66 + x5 -0.83 0.00 0.00 0.00 1.00 -0.73 0.06 0.00 0.00 -0.36 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 -0.18 0.00 0.00 0.00 0.00 0.00 0.00 -0.03 0.00 -0.44 -0.06 0.44 5.05 + w18 -2.16 0.00 0.00 0.00 0.00 -6.10 3.59 0.00 0.00 -5.35 0.00 0.00 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 1.00 0.00 -0.31 0.00 0.00 0.00 0.00 0.00 0.00 -1.19 0.00 -0.03 -0.68 0.03 55.26 + w19 0.79 0.00 0.00 0.00 0.00 -2.80 -5.59 0.00 0.00 6.35 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.62 0.00 1.00 -0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.03 0.68 -0.03 56.53 + x2 -0.60 1.00 0.00 0.00 0.00 -1.00 -0.06 0.00 0.00 -0.64 0.00 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 -0.19 0.00 0.00 0.00 0.00 0.00 0.00 -0.23 0.00 -0.56 0.06 0.56 1.42 + w21 -7.32 0.00 0.00 0.00 0.00 -2.96 -2.02 0.00 0.00 0.66 0.00 0.00 -1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.55 0.00 0.00 -1.10 1.00 0.00 0.00 0.00 0.00 0.00 -1.53 0.00 -2.78 -0.08 2.78 50.60 + w22 2.36 0.00 0.00 0.00 0.00 7.69 2.77 0.00 0.00 -0.23 0.00 0.00 1.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.16 0.00 0.00 0.73 0.00 1.00 0.00 0.00 0.00 0.00 -0.15 0.00 2.54 -0.69 -2.54 50.62 + w23 0.47 0.00 0.00 0.00 0.00 12.50 4.40 0.00 0.00 1.82 0.00 0.00 -0.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.39 0.00 0.00 -0.58 0.00 0.00 1.00 0.00 0.00 0.00 1.43 0.00 -1.39 -0.81 1.39 28.41 + w24 5.37 0.00 0.00 0.00 0.00 4.89 2.62 0.00 0.00 -3.67 0.00 0.00 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.55 0.00 0.00 0.30 0.00 0.00 0.00 1.00 0.00 0.00 0.55 0.00 2.28 -0.26 -2.28 45.94 + w25 -3.24 0.00 0.00 0.00 0.00 -6.61 6.49 0.00 0.00 4.24 0.00 0.00 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 -1.10 0.00 0.00 0.00 0.00 1.00 0.00 -1.06 0.00 -2.71 -0.24 2.71 77.97 + w26 4.87 0.00 0.00 0.00 0.00 12.69 9.12 0.00 0.00 3.47 0.00 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 0.00 1.68 0.00 0.00 0.00 0.00 0.00 1.00 -0.36 0.00 2.75 -0.71 -2.75 23.97 + x9 0.31 0.00 0.00 0.00 0.00 0.39 -0.21 -1.00 1.00 -0.17 0.00 0.00 -0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 -0.39 0.04 0.39 3.25 + w28 1.44 0.00 0.00 0.00 0.00 7.22 2.87 0.00 0.00 7.81 0.00 0.00 -0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.41 0.00 0.00 -0.65 0.00 0.00 0.00 0.00 0.00 0.00 1.48 1.00 -0.55 -0.44 0.55 57.49 + x11 -0.05 0.00 0.00 0.00 0.00 -0.34 -0.37 0.00 0.00 0.14 1.00 0.00 -0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.10 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 -0.11 0.08 0.11 0.20 + Z 4.21 0.00 0.00 0.00 0.00 -2.41 3.99 0.00 0.00 -1.43 0.00 0.00 1.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00 0.00 1.19 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 2.911000000.21 999997.09 4.40 + +=== Iteracao: 7 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.22 0.00 0.00 1.00 0.00 0.00 0.64 0.00 0.00 -0.26 0.00 0.00 -0.17 -0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.03 0.00 0.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 -0.49 0.05 0.49 3.50 + w1 -4.48 0.00 0.00 0.00 0.00 0.00 -0.74 0.00 0.00 2.11 0.00 1.00 0.30 0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.28 0.00 0.00 -0.27 0.00 0.00 0.00 0.00 0.00 0.00 -1.19 0.00 -0.51 -0.23 0.51 59.57 + x3 -0.07 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.09 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.21 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.07 0.00 -0.07 1.74 + x6 0.91 0.00 0.00 0.00 0.00 1.00 0.02 0.00 0.00 0.79 0.00 0.00 0.18 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 -0.17 0.00 0.35 0.02 -0.35 1.50 + w4 -1.15 0.00 0.00 0.00 0.00 0.00 2.80 0.00 0.00 -2.90 0.00 0.00 -3.08 -2.12 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.23 0.00 0.00 -2.88 0.00 0.00 0.00 0.00 0.00 0.00 3.52 0.00 -4.18 0.29 4.18 12.92 + w5 3.86 0.00 0.00 0.00 0.00 0.00 5.04 0.00 0.00 4.93 0.00 0.00 0.65 0.21 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.41 0.00 0.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 -1.02 0.00 0.08 0.43 -0.08 33.08 + w6 -0.95 0.00 0.00 0.00 0.00 0.00 2.76 0.00 0.00 -1.68 0.00 0.00 -0.27 -0.30 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.51 0.00 0.00 -1.04 0.00 0.00 0.00 0.00 0.00 0.00 2.19 0.00 0.30 -0.63 -0.30 12.49 + w7 3.10 0.00 0.00 0.00 0.00 0.00 -0.42 0.00 0.00 -5.01 0.00 0.00 -2.29 -0.84 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 0.00 -2.11 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 -5.35 0.38 5.35 38.62 + w8 -4.08 0.00 0.00 0.00 0.00 0.00 6.01 0.00 0.00 -1.54 0.00 0.00 -0.78 -1.03 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 -0.87 0.00 0.00 0.00 0.00 0.00 0.00 1.11 0.00 -1.34 -0.81 1.34 1.90 + w9 -2.61 0.00 0.00 0.00 0.00 0.00 1.11 0.00 0.00 -5.48 0.00 0.00 -1.68 0.13 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.00 0.00 -1.05 0.00 0.00 0.00 0.00 0.00 0.00 -1.62 0.00 -4.23 -0.11 4.23 78.95 + w10 -8.74 0.00 0.00 0.00 0.00 0.00 6.97 0.00 0.00 -6.86 0.00 0.00 -3.63 -1.41 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.81 0.00 0.00 -3.49 0.00 0.00 0.00 0.00 0.00 0.00 1.38 0.00 -8.37 -0.05 8.37 60.01 + w11 -8.16 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 -13.90 0.00 0.00 -3.37 -1.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.98 0.00 0.00 0.00 0.00 0.00 0.00 1.03 0.00 -4.81 -1.05 4.81 66.71 + w12 -1.90 0.00 0.00 0.00 0.00 0.00 4.10 0.00 0.00 0.80 0.00 0.00 0.15 -0.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.14 0.00 0.00 -0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.91 0.00 0.14 0.25 -0.14 28.53 + w13 5.18 0.00 0.00 0.00 0.00 0.00 4.23 0.00 0.00 0.95 0.00 0.00 -2.56 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.81 0.00 0.00 -1.95 0.00 0.00 0.00 0.00 0.00 0.00 -1.21 0.00 -5.87 0.26 5.87 87.89 + w14 0.09 0.00 0.00 0.00 0.00 0.00 1.43 0.00 0.00 -5.06 0.00 0.00 -0.05 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.91 0.00 0.00 -1.03 0.00 0.00 0.00 0.00 0.00 0.00 -0.14 0.00 -1.48 0.47 1.48 8.70 + w15 -0.17 0.00 0.00 0.00 0.00 0.00 1.19 0.00 0.00 -7.01 0.00 0.00 -2.94 -0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.68 0.00 0.00 -1.75 0.00 0.00 0.00 0.00 0.00 0.00 -1.01 0.00 -6.47 0.36 6.47 38.84 + w16 -2.97 0.00 0.00 0.00 0.00 0.00 8.12 0.00 0.00 -2.17 0.00 0.00 -0.35 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.24 0.00 0.00 -1.03 0.00 0.00 0.00 0.00 0.00 0.00 -0.58 0.00 -2.44 -0.07 2.44 36.81 + x5 -0.16 0.00 0.00 0.00 1.00 0.00 0.08 0.00 0.00 0.22 0.00 0.00 -0.02 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 0.00 -0.15 0.00 -0.19 -0.04 0.19 6.15 + w18 3.36 0.00 0.00 0.00 0.00 0.00 3.72 0.00 0.00 -0.53 0.00 0.00 1.75 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.64 1.00 0.00 0.99 0.00 0.00 0.00 0.00 0.00 0.00 -2.21 0.00 2.09 -0.55 -2.09 64.42 + w19 3.32 0.00 0.00 0.00 0.00 0.00 -5.53 0.00 0.00 8.56 0.00 0.00 0.37 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.66 0.00 1.00 -0.05 0.00 0.00 0.00 0.00 0.00 0.00 -0.39 0.00 1.00 0.74 -1.00 60.73 + x2 0.31 1.00 0.00 0.00 0.00 0.00 -0.04 0.00 0.00 0.15 0.00 0.00 0.02 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 -0.40 0.00 -0.21 0.08 0.21 2.93 + w21 -4.64 0.00 0.00 0.00 0.00 0.00 -1.95 0.00 0.00 3.00 0.00 0.00 -1.12 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.51 0.00 0.00 -0.47 1.00 0.00 0.00 0.00 0.00 0.00 -2.02 0.00 -1.75 -0.02 1.75 55.04 + w22 -4.60 0.00 0.00 0.00 0.00 0.00 2.60 0.00 0.00 -6.30 0.00 0.00 -0.09 -1.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.05 0.00 0.00 -0.90 0.00 1.00 0.00 0.00 0.00 0.00 1.14 0.00 -0.13 -0.85 0.13 39.08 + w23 -10.85 0.00 0.00 0.00 0.00 0.00 4.12 0.00 0.00 -8.06 0.00 0.00 -3.29 -1.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.22 0.00 0.00 -3.23 0.00 0.00 1.00 0.00 0.00 0.00 3.53 0.00 -5.72 -1.07 5.72 9.63 + w24 0.95 0.00 0.00 0.00 0.00 0.00 2.51 0.00 0.00 -7.53 0.00 0.00 -0.52 -0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.48 0.00 0.00 -0.73 0.00 0.00 0.00 1.00 0.00 0.00 1.37 0.00 0.58 -0.36 -0.58 38.60 + w25 2.75 0.00 0.00 0.00 0.00 0.00 6.64 0.00 0.00 9.46 0.00 0.00 1.12 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.30 0.00 0.00 0.00 0.00 1.00 0.00 -2.17 0.00 -0.41 -0.11 0.41 87.90 + w26 -6.62 0.00 0.00 0.00 0.00 0.00 8.83 0.00 0.00 -6.55 0.00 0.00 -1.59 -1.74 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 -1.02 0.00 0.00 0.00 0.00 0.00 1.00 1.77 0.00 -1.65 -0.97 1.65 4.91 + x9 -0.04 0.00 0.00 0.00 0.00 0.00 -0.21 -1.00 1.00 -0.48 0.00 0.00 -0.34 -0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 -0.52 0.03 0.52 2.66 + w28 -5.10 0.00 0.00 0.00 0.00 0.00 2.71 0.00 0.00 2.11 0.00 0.00 -1.61 -0.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.31 0.00 0.00 -2.18 0.00 0.00 0.00 0.00 0.00 0.00 2.70 1.00 -3.06 -0.59 3.06 46.65 + x11 0.26 0.00 0.00 0.00 0.00 0.00 -0.36 0.00 0.00 0.41 1.00 0.00 -0.03 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 -0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.01 0.09 -0.01 0.71 + Z 6.39 0.00 0.00 0.00 0.00 0.00 4.04 0.00 0.00 0.47 0.00 0.00 2.09 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 1.70 0.00 0.00 0.00 0.00 0.00 0.00 -0.41 0.00 3.741000000.26 999996.26 8.02 + +=== Iteracao: 8 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.35 0.00 0.00 1.00 0.00 0.00 0.45 0.00 0.00 -0.21 0.00 0.00 -0.14 -0.05 0.00 0.00 0.00 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.03 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.44 0.08 0.44 3.44 + w1 -8.85 0.00 0.00 0.00 0.00 0.00 5.69 0.00 0.00 0.47 0.00 1.00 -0.53 -0.26 0.00 0.00 0.00 0.00 1.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.22 0.00 0.00 -1.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.95 -1.11 1.95 61.61 + x3 0.57 0.00 1.00 0.00 0.00 0.00 -0.94 0.00 0.00 0.41 0.00 0.00 0.21 0.19 0.00 0.00 0.00 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.22 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.13 -0.28 1.44 + x6 0.29 0.00 0.00 0.00 0.00 1.00 0.93 0.00 0.00 0.56 0.00 0.00 0.07 -0.02 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 -0.10 -0.14 1.79 + w4 11.79 0.00 0.00 0.00 0.00 0.00 -16.27 0.00 0.00 1.98 0.00 0.00 -0.60 1.15 1.00 0.00 0.00 0.00 -3.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.42 0.00 0.00 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 2.88 -0.08 6.88 + w5 0.13 0.00 0.00 0.00 0.00 0.00 10.54 0.00 0.00 3.52 0.00 0.00 -0.07 -0.73 0.00 1.00 0.00 0.00 0.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.36 0.00 0.00 -0.94 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.15 -0.32 1.15 34.82 + w6 7.11 0.00 0.00 0.00 0.00 0.00 -9.11 0.00 0.00 1.36 0.00 0.00 1.27 1.73 0.00 0.00 1.00 0.00 -1.98 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.62 0.00 0.00 0.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.95 0.98 -2.95 8.73 + w7 3.65 0.00 0.00 0.00 0.00 0.00 -1.24 0.00 0.00 -4.80 0.00 0.00 -2.19 -0.70 0.00 0.00 0.00 1.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 -1.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -5.17 0.49 5.17 38.37 + w27 -3.67 0.00 0.00 0.00 0.00 0.00 5.41 0.00 0.00 -1.39 0.00 0.00 -0.70 -0.93 0.00 0.00 0.00 0.00 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 -0.79 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -1.21 -0.73 1.21 1.72 + w9 -8.56 0.00 0.00 0.00 0.00 0.00 9.87 0.00 0.00 -7.72 0.00 0.00 -2.82 -1.37 0.00 0.00 0.00 0.00 1.46 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.00 0.00 -2.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -6.18 -1.29 6.18 81.72 + w10 -3.67 0.00 0.00 0.00 0.00 0.00 -0.50 0.00 0.00 -4.95 0.00 0.00 -2.66 -0.13 0.00 0.00 0.00 0.00 -1.25 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.89 0.00 0.00 -2.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -6.70 0.96 6.70 57.64 + w11 -4.39 0.00 0.00 0.00 0.00 0.00 -5.54 0.00 0.00 -12.48 0.00 0.00 -2.65 -0.47 0.00 0.00 0.00 0.00 -0.93 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.94 0.00 0.00 -1.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -3.57 -0.29 3.57 64.95 + w12 1.46 0.00 0.00 0.00 0.00 0.00 -0.85 0.00 0.00 2.06 0.00 0.00 0.79 0.14 0.00 0.00 0.00 0.00 -0.82 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.19 0.00 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.24 0.92 -1.24 26.96 + w13 0.75 0.00 0.00 0.00 0.00 0.00 10.77 0.00 0.00 -0.73 0.00 0.00 -3.41 -1.06 0.00 0.00 0.00 0.00 1.09 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.87 0.00 0.00 -2.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -7.33 -0.63 7.33 89.97 + w14 -0.42 0.00 0.00 0.00 0.00 0.00 2.18 0.00 0.00 -5.26 0.00 0.00 -0.15 -0.27 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.90 0.00 0.00 -1.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.65 0.37 1.65 8.94 + w15 -3.90 0.00 0.00 0.00 0.00 0.00 6.68 0.00 0.00 -8.42 0.00 0.00 -3.65 -1.84 0.00 0.00 0.00 0.00 0.91 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.73 0.00 0.00 -2.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -7.70 -0.38 7.70 40.58 + w16 -5.11 0.00 0.00 0.00 0.00 0.00 11.28 0.00 0.00 -2.98 0.00 0.00 -0.76 -0.44 0.00 0.00 0.00 0.00 0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.21 0.00 0.00 -1.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -3.15 -0.50 3.15 37.81 + x5 -0.72 0.00 0.00 0.00 1.00 0.00 0.90 0.00 0.00 0.01 0.00 0.00 -0.12 -0.04 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.37 -0.15 0.37 6.41 + w18 -4.76 0.00 0.00 0.00 0.00 0.00 15.68 0.00 0.00 -3.59 0.00 0.00 0.19 -1.21 0.00 0.00 0.00 0.00 1.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.76 1.00 0.00 -0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.58 -2.17 0.58 68.21 + w19 1.89 0.00 0.00 0.00 0.00 0.00 -3.42 0.00 0.00 8.02 0.00 0.00 0.10 0.02 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.64 0.00 1.00 -0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.52 0.45 -0.52 61.40 + x2 -1.17 1.00 0.00 0.00 0.00 0.00 2.14 0.00 0.00 -0.41 0.00 0.00 -0.26 -0.24 0.00 0.00 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 -0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.70 -0.22 0.70 3.62 + w21 -12.07 0.00 0.00 0.00 0.00 0.00 9.00 0.00 0.00 0.19 0.00 0.00 -2.55 -1.47 0.00 0.00 0.00 0.00 1.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.61 0.00 0.00 -2.06 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -4.20 -1.51 4.20 58.51 + w22 -0.41 0.00 0.00 0.00 0.00 0.00 -3.58 0.00 0.00 -4.72 0.00 0.00 0.71 0.01 0.00 0.00 0.00 0.00 -1.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 -0.01 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.25 -0.02 -1.25 37.12 + w23 2.13 0.00 0.00 0.00 0.00 0.00 -15.00 0.00 0.00 -3.16 0.00 0.00 -0.80 1.56 0.00 0.00 0.00 0.00 -3.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.40 0.00 0.00 -0.45 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -1.46 1.52 1.46 3.57 + w24 5.99 0.00 0.00 0.00 0.00 0.00 -4.92 0.00 0.00 -5.62 0.00 0.00 0.45 0.60 0.00 0.00 0.00 0.00 -1.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.55 0.00 0.00 0.34 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 2.24 0.64 -2.24 36.24 + w25 -5.22 0.00 0.00 0.00 0.00 0.00 18.39 0.00 0.00 6.45 0.00 0.00 -0.41 -1.11 0.00 0.00 0.00 0.00 1.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.38 0.00 0.00 -1.40 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -3.04 -1.70 3.04 91.63 + w26 -0.10 0.00 0.00 0.00 0.00 0.00 -0.77 0.00 0.00 -4.09 0.00 0.00 -0.34 -0.09 0.00 0.00 0.00 0.00 -1.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.38 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.49 0.33 -0.49 1.86 + x9 0.54 0.00 0.00 0.00 0.00 0.00 -1.07 -1.00 1.00 -0.27 0.00 0.00 -0.23 0.09 0.00 0.00 0.00 0.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 -0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.14 0.33 2.39 + w28 4.81 0.00 0.00 0.00 0.00 0.00 -11.89 0.00 0.00 5.84 0.00 0.00 0.29 1.51 0.00 0.00 0.00 0.00 -2.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.45 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.20 1.39 -0.20 42.02 + x11 0.67 0.00 0.00 0.00 0.00 0.00 -0.97 0.00 0.00 0.57 1.00 0.00 0.05 0.15 0.00 0.00 0.00 0.00 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.17 -0.15 0.52 + Z 4.87 0.00 0.00 0.00 0.00 0.00 6.27 0.00 0.00 -0.10 0.00 0.00 1.80 -0.05 0.00 0.00 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.00 1.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.24 999999.96 999996.76 8.72 + +=== Iteracao: 9 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.60 0.00 0.00 1.00 0.00 0.00 0.08 0.00 0.00 0.00 0.37 0.00 -0.13 0.01 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.08 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.39 0.14 0.39 3.63 + w1 -9.41 0.00 0.00 0.00 0.00 0.00 6.49 0.00 0.00 0.00 -0.82 1.00 -0.57 -0.39 0.00 0.00 0.00 0.00 1.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.12 0.00 0.00 -1.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.07 -1.25 2.07 61.19 + x3 0.09 0.00 1.00 0.00 0.00 0.00 -0.25 0.00 0.00 0.00 -0.72 0.00 0.18 0.08 0.00 0.00 0.00 0.00 -0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.14 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.01 -0.18 1.06 + x6 -0.37 0.00 0.00 0.00 0.00 1.00 1.88 0.00 0.00 0.00 -0.98 0.00 0.02 -0.17 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.27 -0.00 1.28 + w4 9.44 0.00 0.00 0.00 0.00 0.00 -12.88 0.00 0.00 0.00 -3.49 0.00 -0.77 0.62 1.00 0.00 0.00 0.00 -2.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.03 0.00 0.00 -0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.44 2.27 0.44 5.07 + w5 -4.05 0.00 0.00 0.00 0.00 0.00 16.55 0.00 0.00 0.00 -6.19 0.00 -0.37 -1.66 0.00 1.00 0.00 0.00 1.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.00 -1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.06 -1.40 2.06 31.61 + w6 5.49 0.00 0.00 0.00 0.00 0.00 -6.79 0.00 0.00 0.00 -2.39 0.00 1.16 1.37 0.00 0.00 1.00 0.00 -1.74 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.36 0.00 0.00 0.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.60 0.56 -2.60 7.49 + w7 9.35 0.00 0.00 0.00 0.00 0.00 -9.43 0.00 0.00 0.00 8.44 0.00 -1.78 0.57 0.00 0.00 0.00 1.00 -0.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.02 0.00 0.00 -1.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -3.94 1.96 3.94 42.74 + w27 -2.03 0.00 0.00 0.00 0.00 0.00 3.05 0.00 0.00 0.00 2.44 0.00 -0.59 -0.56 0.00 0.00 0.00 0.00 0.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.22 0.00 0.00 -0.76 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.85 -0.31 0.85 2.98 + w9 0.60 0.00 0.00 0.00 0.00 0.00 -3.31 0.00 0.00 0.00 13.58 0.00 -2.17 0.67 0.00 0.00 0.00 0.00 0.09 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 0.00 -2.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -4.20 1.07 4.20 88.76 + w10 2.21 0.00 0.00 0.00 0.00 0.00 -8.96 0.00 0.00 0.00 8.71 0.00 -2.24 1.18 0.00 0.00 0.00 0.00 -2.12 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.86 0.00 0.00 -2.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -5.42 2.48 5.42 62.16 + w11 10.42 0.00 0.00 0.00 0.00 0.00 -26.85 0.00 0.00 0.00 21.95 0.00 -1.60 2.83 0.00 0.00 0.00 0.00 -3.14 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -1.51 0.00 0.00 -0.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.36 3.53 0.36 76.33 + w12 -0.99 0.00 0.00 0.00 0.00 0.00 2.68 0.00 0.00 0.00 -3.63 0.00 0.62 -0.41 0.00 0.00 0.00 0.00 -0.46 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -0.79 0.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 0.29 -0.71 25.08 + w13 1.61 0.00 0.00 0.00 0.00 0.00 9.53 0.00 0.00 0.00 1.28 0.00 -3.35 -0.87 0.00 0.00 0.00 0.00 0.96 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.73 0.00 0.00 -2.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -7.14 -0.40 7.14 90.63 + w14 5.82 0.00 0.00 0.00 0.00 0.00 -6.80 0.00 0.00 0.00 9.24 0.00 0.29 1.12 0.00 0.00 0.00 0.00 -0.81 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -1.93 0.00 0.00 -1.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.29 1.98 0.29 13.73 + w15 6.09 0.00 0.00 0.00 0.00 0.00 -7.69 0.00 0.00 0.00 14.81 0.00 -2.94 0.39 0.00 0.00 0.00 0.00 -0.58 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.92 0.00 0.00 -2.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -5.53 2.19 5.53 48.26 + w16 -1.58 0.00 0.00 0.00 0.00 0.00 6.20 0.00 0.00 0.00 5.24 0.00 -0.51 0.35 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.79 0.00 0.00 -1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.38 0.41 2.38 40.53 + x5 -0.73 0.00 0.00 0.00 1.00 0.00 0.92 0.00 0.00 0.00 -0.02 0.00 -0.12 -0.04 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.37 -0.16 0.37 6.40 + w18 -0.49 0.00 0.00 0.00 0.00 0.00 9.55 0.00 0.00 0.00 6.32 0.00 0.49 -0.26 0.00 0.00 0.00 0.00 1.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 1.00 0.00 -0.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 -1.07 -0.34 71.49 + w19 -7.62 0.00 0.00 0.00 0.00 0.00 10.27 0.00 0.00 0.00 -14.10 0.00 -0.58 -2.10 0.00 0.00 0.00 0.00 1.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.93 0.00 1.00 -0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.54 -2.00 1.54 54.09 + x2 -0.68 1.00 0.00 0.00 0.00 0.00 1.44 0.00 0.00 0.00 0.72 0.00 -0.23 -0.13 0.00 0.00 0.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 -0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.59 -0.09 0.59 3.99 + w21 -12.30 0.00 0.00 0.00 0.00 0.00 9.33 0.00 0.00 0.00 -0.34 0.00 -2.57 -1.52 0.00 0.00 0.00 0.00 1.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.65 0.00 0.00 -2.07 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -4.25 -1.57 4.25 58.34 + w22 5.20 0.00 0.00 0.00 0.00 0.00 -11.64 0.00 0.00 0.00 8.31 0.00 1.11 1.25 0.00 0.00 0.00 0.00 -1.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.04 0.00 0.00 0.07 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 2.47 1.43 -2.47 41.42 + w23 5.88 0.00 0.00 0.00 0.00 0.00 -20.40 0.00 0.00 0.00 5.56 0.00 -0.54 2.40 0.00 0.00 0.00 0.00 -3.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -2.02 0.00 0.00 -0.40 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 -0.64 2.49 0.64 6.46 + w24 12.67 0.00 0.00 0.00 0.00 0.00 -14.52 0.00 0.00 0.00 9.89 0.00 0.92 2.09 0.00 0.00 0.00 0.00 -2.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.66 0.00 0.00 0.44 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 3.69 2.37 -3.69 41.37 + w25 -12.88 0.00 0.00 0.00 0.00 0.00 29.41 0.00 0.00 0.00 -11.35 0.00 -0.95 -2.81 0.00 0.00 0.00 0.00 3.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.64 0.00 0.00 -1.51 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -4.70 -3.67 4.70 85.74 + w26 4.76 0.00 0.00 0.00 0.00 0.00 -7.76 0.00 0.00 0.00 7.20 0.00 0.00 0.99 0.00 0.00 0.00 0.00 -2.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.79 0.00 0.00 0.45 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.55 1.58 -1.55 5.60 + x9 0.85 0.00 0.00 0.00 0.00 0.00 -1.52 -1.00 1.00 0.00 0.47 0.00 -0.21 0.16 0.00 0.00 0.00 0.00 -0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.13 0.00 0.00 -0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.27 0.22 0.27 2.63 + w28 -2.12 0.00 0.00 0.00 0.00 0.00 -1.91 0.00 0.00 0.00 -10.28 0.00 -0.20 -0.03 0.00 0.00 0.00 0.00 -1.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.31 0.00 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -1.30 -0.40 1.30 36.69 + x10 1.19 0.00 0.00 0.00 0.00 0.00 -1.71 0.00 0.00 1.00 1.76 0.00 0.08 0.26 0.00 0.00 0.00 0.00 -0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.31 -0.26 0.91 + Z 4.99 0.00 0.00 0.00 0.00 0.00 6.10 0.00 0.00 0.00 0.18 0.00 1.81 -0.03 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 1.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.27 999999.99 999996.73 8.82 + +=== Iteracao: 10 === + VB x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 w26 w27 w28 w29 a1 a2 b + x4 0.58 0.00 0.00 1.00 0.00 0.00 0.16 0.00 0.00 0.00 0.35 0.00 -0.12 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 -0.11 0.00 0.00 -0.00 0.00 0.00 0.00 0.00 0.00 -0.39 0.13 0.39 3.61 + w1 -8.45 0.00 0.00 0.00 0.00 0.00 3.19 0.00 0.00 0.00 0.08 1.00 -0.66 0.00 0.00 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.45 0.00 0.00 -1.27 0.00 0.00 0.16 0.00 0.00 0.00 0.00 0.00 -2.17 -0.85 2.17 62.23 + x3 -0.12 0.00 1.00 0.00 0.00 0.00 0.46 0.00 0.00 0.00 -0.91 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.07 0.00 0.00 0.08 0.00 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 0.20 -0.08 -0.20 0.84 + x6 0.03 0.00 0.00 0.00 0.00 1.00 0.47 0.00 0.00 0.00 -0.59 0.00 -0.02 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.04 0.00 0.00 0.04 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 -0.04 -0.10 0.04 1.73 + w4 7.92 0.00 0.00 0.00 0.00 0.00 -7.61 0.00 0.00 0.00 -4.93 0.00 -0.63 0.00 1.00 0.00 0.00 0.00 -1.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.51 0.00 0.00 -0.03 0.00 0.00 -0.26 0.00 0.00 0.00 0.00 0.00 -0.27 1.62 0.27 3.40 + w5 0.03 0.00 0.00 0.00 0.00 0.00 2.41 0.00 0.00 0.00 -2.33 0.00 -0.74 0.00 0.00 1.00 0.00 0.00 -1.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -1.07 0.00 0.00 -1.28 0.00 0.00 0.69 0.00 0.00 0.00 0.00 0.00 -2.50 0.33 2.50 36.09 + w6 2.13 0.00 0.00 0.00 0.00 0.00 4.86 0.00 0.00 0.00 -5.57 0.00 1.46 0.00 0.00 0.00 1.00 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.89 0.00 0.00 -0.57 0.00 0.00 0.00 0.00 0.00 2.97 -0.86 -2.97 3.80 + w7 7.96 0.00 0.00 0.00 0.00 0.00 -4.59 0.00 0.00 0.00 7.12 0.00 -1.66 0.00 0.00 0.00 0.00 1.00 -0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.54 0.00 0.00 -1.81 0.00 0.00 -0.24 0.00 0.00 0.00 0.00 0.00 -3.79 1.37 3.79 41.21 + w27 -0.66 0.00 0.00 0.00 0.00 0.00 -1.71 0.00 0.00 0.00 3.74 0.00 -0.71 0.00 0.00 0.00 0.00 0.00 -0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.69 0.00 0.00 -0.86 0.00 0.00 0.23 0.00 0.00 0.00 1.00 0.00 -1.00 0.27 1.00 4.49 + w9 -1.04 0.00 0.00 0.00 0.00 0.00 2.37 0.00 0.00 0.00 12.03 0.00 -2.02 0.00 0.00 0.00 0.00 0.00 1.13 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.30 0.00 0.00 -2.08 0.00 0.00 -0.28 0.00 0.00 0.00 0.00 0.00 -4.02 0.38 4.02 86.97 + w10 -0.69 0.00 0.00 0.00 0.00 0.00 1.09 0.00 0.00 0.00 5.97 0.00 -1.98 0.00 0.00 0.00 0.00 0.00 -0.28 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.86 0.00 0.00 -2.12 0.00 0.00 -0.49 0.00 0.00 0.00 0.00 0.00 -5.11 1.26 5.11 58.98 + w11 3.47 0.00 0.00 0.00 0.00 0.00 -2.74 0.00 0.00 0.00 15.38 0.00 -0.96 0.00 0.00 0.00 0.00 0.00 1.28 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.88 0.00 0.00 -0.49 0.00 0.00 -1.18 0.00 0.00 0.00 0.00 0.00 0.40 0.59 -0.40 68.71 + w12 0.01 0.00 0.00 0.00 0.00 0.00 -0.79 0.00 0.00 0.00 -2.69 0.00 0.53 0.00 0.00 0.00 0.00 0.00 -1.09 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 -1.13 0.00 0.00 0.22 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.60 0.71 -0.60 26.18 + w13 3.74 0.00 0.00 0.00 0.00 0.00 2.15 0.00 0.00 0.00 3.29 0.00 -3.54 0.00 0.00 0.00 0.00 0.00 -0.40 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 -0.00 0.00 0.00 -3.04 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.00 -7.37 0.50 7.37 92.97 + w14 3.07 0.00 0.00 0.00 0.00 0.00 2.75 0.00 0.00 0.00 6.64 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.94 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 -0.99 0.00 0.00 -0.86 0.00 0.00 -0.47 0.00 0.00 0.00 0.00 0.00 0.01 0.82 -0.01 10.71 + w15 5.14 0.00 0.00 0.00 0.00 0.00 -4.39 0.00 0.00 0.00 13.91 0.00 -2.86 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 -0.59 0.00 0.00 -2.34 0.00 0.00 -0.16 0.00 0.00 0.00 0.00 0.00 -5.43 1.79 5.43 47.22 + w16 -2.44 0.00 0.00 0.00 0.00 0.00 9.18 0.00 0.00 0.00 4.42 0.00 -0.44 0.00 0.00 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 -0.50 0.00 0.00 -1.38 0.00 0.00 -0.15 0.00 0.00 0.00 0.00 0.00 -2.29 0.05 2.29 39.58 + x5 -0.63 0.00 0.00 0.00 1.00 0.00 0.55 0.00 0.00 0.00 0.08 0.00 -0.13 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 -0.15 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 -0.38 -0.11 0.38 6.52 + w18 0.15 0.00 0.00 0.00 0.00 0.00 7.32 0.00 0.00 0.00 6.93 0.00 0.43 0.00 0.00 0.00 0.00 0.00 0.94 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.17 1.00 0.00 -0.73 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.27 -0.80 -0.27 72.19 + w19 -2.48 0.00 0.00 0.00 0.00 0.00 -7.58 0.00 0.00 0.00 -9.24 0.00 -1.05 0.00 0.00 0.00 0.00 0.00 -1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.84 0.00 1.00 -0.84 0.00 0.00 0.87 0.00 0.00 0.00 0.00 0.00 -2.10 0.17 2.10 59.73 + x2 -0.37 1.00 0.00 0.00 0.00 0.00 0.35 0.00 0.00 0.00 1.02 0.00 -0.26 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.02 0.00 0.00 -0.30 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 -0.63 0.04 0.63 4.34 + w21 -8.57 0.00 0.00 0.00 0.00 0.00 -3.60 0.00 0.00 0.00 3.19 0.00 -2.91 0.00 0.00 0.00 0.00 0.00 -0.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.37 0.00 0.00 -2.32 1.00 0.00 0.63 0.00 0.00 0.00 0.00 0.00 -4.65 0.01 4.65 62.43 + w22 2.12 0.00 0.00 0.00 0.00 0.00 -0.97 0.00 0.00 0.00 5.40 0.00 1.39 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.28 0.00 1.00 -0.52 0.00 0.00 0.00 0.00 0.00 2.80 0.13 -2.80 38.05 + w3 2.45 0.00 0.00 0.00 0.00 0.00 -8.50 0.00 0.00 0.00 2.32 0.00 -0.22 1.00 0.00 0.00 0.00 0.00 -1.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.84 0.00 0.00 -0.17 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00 -0.27 1.04 0.27 2.69 + w24 7.54 0.00 0.00 0.00 0.00 0.00 3.26 0.00 0.00 0.00 5.04 0.00 1.39 0.00 0.00 0.00 0.00 0.00 1.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.79 0.00 0.00 -0.87 1.00 0.00 0.00 0.00 0.00 4.25 0.20 -4.25 35.74 + w25 -5.99 0.00 0.00 0.00 0.00 0.00 5.49 0.00 0.00 0.00 -4.83 0.00 -1.58 0.00 0.00 0.00 0.00 0.00 -1.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.73 0.00 0.00 -1.98 0.00 0.00 1.17 0.00 1.00 0.00 0.00 0.00 -5.45 -0.76 5.45 93.31 + w26 2.33 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 4.90 0.00 0.22 0.00 0.00 0.00 0.00 0.00 -0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.61 0.00 0.00 -0.41 0.00 0.00 1.00 0.00 0.00 1.81 0.56 -1.81 2.93 + x9 0.45 0.00 0.00 0.00 0.00 0.00 -0.14 -1.00 1.00 0.00 0.09 0.00 -0.17 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 -0.05 0.00 0.00 -0.07 0.00 0.00 0.00 0.00 0.00 -0.22 0.06 0.22 2.20 + w28 -2.04 0.00 0.00 0.00 0.00 0.00 -2.20 0.00 0.00 0.00 -10.20 0.00 -0.21 0.00 0.00 0.00 0.00 0.00 -1.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.33 0.00 0.00 -0.17 0.00 0.00 0.01 0.00 0.00 0.00 0.00 1.00 -1.31 -0.36 1.31 36.78 + x10 0.54 0.00 0.00 0.00 0.00 0.00 0.54 0.00 0.00 1.00 1.15 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.06 0.00 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 0.33 0.03 -0.33 0.20 + Z 5.06 0.00 0.00 0.00 0.00 0.00 5.88 0.00 0.00 0.00 0.24 0.00 1.80 0.00 0.00 0.00 0.00 0.00 0.31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.00 0.00 1.38 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 3.261000000.02 999996.74 8.89 + +================================================================================ +Solução + +FO: 8.8870 +x1 = 0.0000 +x2 = -4.3388 +x3 = -0.8407 +x4 = 3.6057 +x5 = 6.5191 +x6 = 1.7292 +x7 = 0.0000 +x8 = -2.1956 +x9 = 0.2003 +x10 = 0.0000 + +R1 = 5.0000 <= 5.0000 <= 5.0000 +R2 = None <= -2.2321 <= 60.0000 +R3 = None <= 33.0000 <= 33.0000 +R4 = None <= 54.3092 <= 57.0000 +R5 = None <= 60.6043 <= 64.0000 +R6 = None <= 30.9148 <= 67.0000 +R7 = None <= 31.1974 <= 35.0000 +R8 = None <= 0.7883 <= 42.0000 +R9 = None <= 22.0000 <= 22.0000 +R10 = None <= -12.9650 <= 74.0000 +R11 = None <= -9.9781 <= 49.0000 +R12 = None <= 20.2931 <= 89.0000 +R13 = None <= 46.8214 <= 73.0000 +R14 = None <= -28.9666 <= 64.0000 +R15 = None <= 25.2878 <= 36.0000 +R16 = None <= -3.2153 <= 44.0000 +R17 = None <= -10.5820 <= 29.0000 +R18 = None <= 28.0000 <= 28.0000 +R19 = None <= 14.8055 <= 87.0000 +R20 = None <= 40.2664 <= 100.0000 +R21 = None <= 43.0000 <= 43.0000 +R22 = None <= 15.5688 <= 78.0000 +R23 = None <= 48.9527 <= 87.0000 +R24 = None <= 7.0000 <= 7.0000 +R25 = None <= 61.2576 <= 97.0000 +R26 = None <= -25.3125 <= 68.0000 +R27 = None <= 51.0702 <= 54.0000 +R28 = None <= 43.5139 <= 48.0000 +R29 = None <= 7.2213 <= 44.0000 +R30 = 38.0000 <= 38.0000 <= None diff --git a/main/parser.py b/main/parser.py index edd8126..17c0465 100644 --- a/main/parser.py +++ b/main/parser.py @@ -16,13 +16,25 @@ def __init__(self, filepath): self.constraints = [] self.num_vars = 0 self.non_negative = [] + self.var_signs = [] def parse(self): lines = [line.strip() for line in open(self.filepath)] - self.parse_objective(lines[0]) + idx = None + for i, l in enumerate(lines): + ll = l.lower() + if ll.startswith("max") or ll.startswith("min"): + idx = i + break + if idx is None: + raise ValueError("Objective function must be Max or Min.") + + self.parse_objective(lines[idx]) - for line in lines[1:]: + for line in lines[idx+1:]: + if not line: + continue if self.is_non_negative(line): self.parse_non_negative(line) elif "<=" in line or ">=" in line or "=" in line: @@ -31,32 +43,63 @@ def parse(self): #if some variables were not mentioned in non-negativity constraints, assume they are non-negative while len(self.non_negative) < self.num_vars: self.non_negative.append(True) + while len(self.var_signs) < self.num_vars: + self.var_signs.append(">=0") return { "objective_type": self.objective_type, "objective_coeffs": self.objective_coeffs, "constraints": self.constraints, "num_vars": self.num_vars, - "non_negative": self.non_negative + "non_negative": self.non_negative, + "var_signs": self.var_signs } def is_non_negative(self, line): - return re.match(r"x\d+\s*>=\s*0$", line) is not None + """ + x\d+\s*(>=|<=)\s*0$: + x\d+ - 'x' followed by one or more digits (variable index, e.g. x1, x2) + \s* - any number of spaces + (>=|<=) - either the ">=" or "<=" comparison operator + \s* - any number of spaces + 0 - the numeric bound zero + $ - end of line anchor to ensure the entire tail matches (so it wont match extra trailing chars) + + This pattern matches lines like: "x1 >= 0" or "x2 <= 0". + In addition, we also treat lines containing the word "livre" (Portuguese for "free") + as indicating a free variable (no sign restriction). + """ + return re.match(r"x\d+\s*(>=|<=)\s*0$", line) is not None or "livre" in line.lower() def parse_non_negative(self, line): - var = int(re.findall(r"x(\d+)", line)[0]) + var_match = re.findall(r"x(\d+)", line) + if not var_match: + return + + var = int(var_match[0]) self.num_vars = max(self.num_vars, var) while len(self.non_negative) < var: self.non_negative.append(True) - - self.non_negative[var - 1] = True + while len(self.var_signs) < var: + self.var_signs.append(">=0") + + if "livre" in line.lower(): + self.non_negative[var - 1] = False + self.var_signs[var - 1] = "free" + elif "<=" in line: + self.non_negative[var - 1] = False + self.var_signs[var - 1] = "<=0" + else: + self.non_negative[var - 1] = True + self.var_signs[var - 1] = ">=0" def parse_objective(self, line): - if line.startswith("Max"): + line_lower = line.lower() + if line_lower.startswith("max"): self.objective_type = "Max" - elif line.startswith("Min"): + elif line_lower.startswith("min"): self.objective_type = "Min" else: raise ValueError("Objective function must be Max or Min.") @@ -67,15 +110,16 @@ def parse_objective(self, line): [+-]? - an optional '+' or '-' sign \s* - followed by any number of spaces \d* - followed by any number of digits (the coefficient itself) - x(\d+) - 2nd step, we look for the variable part: + \s*x\s*(\d+) - 2nd step, we look for the variable part: + \s* - any number of spaces x - the character 'x' indicating a variable + \s* - any number of spaces (\d+) - followed by one or more digits (the variable index, e.g. x1, x2, etc.) """ - - coeffs = re.findall(r'([+-]?\s*\d*)x(\d+)', line) - #after we have found all coefficients and variable indices, we iterate over them to process each pair: + coeffs = re.findall(r'([+-]?\s*\d*)\s*x\s*(\d+)', line) + for raw_coeff, var in coeffs: - raw_coeff = raw_coeff.replace(" ", "") #if we have some spaces, like "+ 3", we remove them + raw_coeff = raw_coeff.replace(" ", "") if raw_coeff == '+' or raw_coeff == '' or raw_coeff == '+ ': raw_coeff = 1 elif raw_coeff == '-': @@ -84,14 +128,12 @@ def parse_objective(self, line): raw_coeff = int(raw_coeff) var = int(var) self.num_vars = max(self.num_vars, var) - #consider an edge case where the coefficients are not in order, like "Max 3x3 + 2x1 + x2" - #we store them as tuples (variable index, coefficient) to sort them later self.objective_coeffs.append((var, raw_coeff)) self.objective_coeffs = [c for _, c in sorted(self.objective_coeffs)] def parse_constraint(self, line): - coeffs = re.findall(r'([+-]?\s*\d*)x(\d+)', line) + coeffs = re.findall(r'([+-]?\s*\d*)\s*x\s*(\d+)', line) parsed = [0] * (self.num_vars) for raw_coeff, var in coeffs: @@ -112,14 +154,13 @@ def parse_constraint(self, line): comp = ">=" else: comp = "=" - + """ (-?\d+)$ -? - matches an optional negative sign \d+ - matches one or more digits $ - guarantees that the match is at the end of the line """ - b = int(re.findall(r'(-?\d+)$', line)[0]) - self.constraints.append((parsed, comp, b)) + self.constraints.append((parsed, comp, b)) \ No newline at end of file diff --git a/main/simplex.py b/main/simplex.py index e69de29..445c627 100644 --- a/main/simplex.py +++ b/main/simplex.py @@ -0,0 +1,316 @@ +from tableau import Tableau +import os + +class Simplex: + def __init__(self, objective_coeffs, constraints, + objective_type="Max", var_signs=None): + """ + Args: + objective_coeffs: objective function coefficients + constraints: List of tuples (coefficients, operator, rhs) + objective_type: "Max" (only maximization is supported) + non_negative: List indicating if each variable is non-negative + """ + self.original_objective_type = objective_type + self.orig_num_vars = len(objective_coeffs) + self.var_signs = var_signs if var_signs else [">=0"] * self.orig_num_vars + self.original_constraints = constraints[:] + + self.c, self.constraints, self._map_orig_to_internal = self.expand_variables( + objective_coeffs, constraints, self.var_signs + ) + + if objective_type == "Min": + self.c = [-float(x) for x in self.c] + self.is_minimization = True + else: + self.c = [float(x) for x in self.c] + self.is_minimization = False + + self.objective_type = "Max" + self.num_vars = len(self.c) + self.M = 1000000 + self.tableau_obj = None + self.artificial_indices = [] + self.solution = None + self.optimal_value = None + self.iteration_logs = [] + + def expand_variables(self, c, constraints, var_signs): + n = len(c) + A = [list(coeffs) for (coeffs, _, _) in constraints] + ops = [op for (_, op, _) in constraints] + b = [rhs for (_, _, rhs) in constraints] + + new_c = [] + mapping = [] + col_blocks = [] + for i in range(n): + sign = var_signs[i] if i < len(var_signs) else ">=0" + if sign == ">=0": + mapping.append([len(new_c)]) + new_c.append(float(c[i])) + col_blocks.append(("single", i, [len(new_c)-1])) + elif sign == "<=0": + mapping.append([len(new_c)]) + new_c.append(float(-c[i])) + col_blocks.append(("neg", i, [len(new_c)-1])) + else: + mapping.append([len(new_c), len(new_c)+1]) + new_c.append(float(c[i])) + new_c.append(float(-c[i])) + col_blocks.append(("free", i, [len(new_c)-2, len(new_c)-1])) + + A_exp = [] + for r in range(len(constraints)): + row = [0.0] * len(new_c) + for i in range(n): + a = float(A[r][i]) + typ, _, idxs = col_blocks[i] + if typ == "single": + row[idxs[0]] = a + elif typ == "neg": + row[idxs[0]] = -a + else: + row[idxs[0]] = a + row[idxs[1]] = -a + A_exp.append(row) + + constraints_exp = [(A_exp[i], ops[i], b[i]) for i in range(len(constraints))] + return new_c, constraints_exp, mapping + + def solve(self): + """ + Solves the LP problem using the Simplex method with Big M. + + Returns: + dict with 'status', 'solution', 'optimal_value' + """ + self.prepare_tableau() + + status = self.simplex_iterations() + + if status == "optimal": + self.extract_solution() + + return { + "status": status, + "solution": self.solution, + "optimal_value": self.optimal_value + } + + def prepare_tableau(self): + """ + Prepares the initial tableau: + - Processes constraints + - Identifies which need slack/artificial variables + - Creates the tableau using the Tableau class + """ + A = [] + b = [] + slack_indices = [] + artificial_indices = [] + slack_types = {} + + for i, (coeffs, op, rhs) in enumerate(self.constraints): + coeffs = [float(x) for x in coeffs] + rhs = float(rhs) + + # if RHS is negative we multiply by -1 and invert the operator + if rhs < 0: + coeffs = [-x for x in coeffs] + rhs = -rhs + if op == "<=": + op = ">=" + elif op == ">=": + op = "<=" + + A.append(coeffs) + b.append(rhs) + + if op == "<=": + # add a slack variable with coefficient +1 + slack_indices.append(i) + slack_types[i] = 1.0 + + elif op == ">=": + # add a slack variable with coefficient -1 and a artificial variable + slack_indices.append(i) + slack_types[i] = -1.0 + artificial_indices.append(i) + + else: # op == "=" + # add only an artificial variable + artificial_indices.append(i) + + self.tableau_obj = Tableau(A, b, self.c) + self.tableau_obj.build_tableau( + slack_indices=slack_indices, + artificial_indices=artificial_indices, + M=self.M, + slack_types=slack_types + ) + + num_slack = len(slack_indices) + for i, idx in enumerate(artificial_indices): + col_idx = self.num_vars + num_slack + i + self.artificial_indices.append(col_idx) + + def simplex_iterations(self): + """ + Returns: + "optimal", "unbounded" or "infeasible" + """ + max_iterations = 1000 + iteration = 0 + + self.iteration_logs.append(self.tableau_obj.format_tableau(iteration=0)) + self.tableau_obj.print_tableau(iteration=0) + + while iteration < max_iterations: + iteration += 1 + + pivot_col = self.find_pivot_column() + if pivot_col is None: + if self.check_artificial_in_basis(): + return "infeasible" + return "optimal" + + pivot_row = self.find_pivot_row(pivot_col) + if pivot_row is None: + return "unbounded" + + self.pivot(pivot_row, pivot_col) + all_var_names = self.tableau_obj.get_all_var_names() + self.tableau_obj.basis[pivot_row] = all_var_names[pivot_col] + + self.iteration_logs.append(self.tableau_obj.format_tableau(iteration=iteration)) + self.tableau_obj.print_tableau(iteration=iteration) + + return "max_iterations_reached" + + def find_pivot_column(self): + """ + Find the pivot column (non-basic variable with most negative coefficient). + Uses Bland's rule to avoid cycling. + + Returns: + Index of the pivot column or None if optimal + """ + obj_row = self.tableau_obj.tableau[-1][:-1] + + min_val = min(obj_row) + + if min_val >= -1e-10: + return None + + for i, val in enumerate(obj_row): + if abs(val - min_val) < 1e-10: + return i + + return None + + def find_pivot_row(self, pivot_col): + m = len(self.tableau_obj.basis) + ratios = [] + + for i in range(m): + if self.tableau_obj.tableau[i][pivot_col] > 1e-10: + ratio = self.tableau_obj.tableau[i][-1] / self.tableau_obj.tableau[i][pivot_col] + ratios.append((ratio, i)) + else: + ratios.append((float('inf'), i)) + + valid_ratios = [(r, i) for r, i in ratios if r >= 0] + + if not valid_ratios: + return None # means that the problem is unbounded + + min_ratio = min(r for r, _ in valid_ratios) + + for r, i in valid_ratios: + if abs(r - min_ratio) < 1e-10: + return i + + return None + + def pivot(self, pivot_row, pivot_col): + pivot = self.tableau_obj.tableau[pivot_row][pivot_col] + num_cols = len(self.tableau_obj.tableau[0]) + + for j in range(num_cols): + self.tableau_obj.tableau[pivot_row][j] /= pivot + + for i in range(len(self.tableau_obj.tableau)): + if i != pivot_row: + multiplier = self.tableau_obj.tableau[i][pivot_col] + for j in range(num_cols): + self.tableau_obj.tableau[i][j] -= multiplier * self.tableau_obj.tableau[pivot_row][j] + + def check_artificial_in_basis(self): + """ + Check for artificial variables in the basis with positive value. + If true, the problem is infeasible. + """ + all_var_names = self.tableau_obj.get_all_var_names() + + for i, var_name in enumerate(self.tableau_obj.basis): + if var_name.startswith('a'): + if self.tableau_obj.tableau[i][-1] > 1e-10: + return True + return False + + def extract_solution(self): + internal_solution = [0.0] * self.num_vars + all_var_names = self.tableau_obj.get_all_var_names() + for i, var_name in enumerate(self.tableau_obj.basis): + if var_name and var_name.startswith('x'): + var_index = int(var_name[1:]) - 1 + internal_solution[var_index] = self.tableau_obj.tableau[i][-1] + + sol = [0.0] * self.orig_num_vars + for i, idxs in enumerate(self._map_orig_to_internal): + if len(idxs) == 1: + if self.var_signs[i] == "<=0": + sol[i] = -internal_solution[idxs[0]] + else: + sol[i] = internal_solution[idxs[0]] + else: + plus, minus = idxs + sol[i] = internal_solution[plus] - internal_solution[minus] + + self.solution = sol + self.optimal_value = self.tableau_obj.tableau[-1][-1] + if self.is_minimization: + self.optimal_value = -self.optimal_value + + def write_report(self, filepath): + os.makedirs(os.path.dirname(filepath), exist_ok=True) + lines = [] + + lines.append("Iterações do Simplex") + lines.append("=" * 80) + lines.extend(self.iteration_logs) + lines.append("=" * 80) + lines.append("Solução") + lines.append("") + lines.append(f"FO: {self.optimal_value:.4f}") + + for i, v in enumerate(self.solution, start=1): + lines.append(f"x{i} = {v:.4f}") + lines.append("") + + for k, (coeffs, op, rhs) in enumerate(self.original_constraints, start=1): + lhs = 0.0 + for j, a in enumerate(coeffs): + lhs += float(a) * float(self.solution[j] if j < len(self.solution) else 0.0) + if op == "<=": + line = f"R{k} = None <= {lhs:.4f} <= {float(rhs):.4f}" + elif op == ">=": + line = f"R{k} = {float(rhs):.4f} <= {lhs:.4f} <= None" + else: + line = f"R{k} = {float(rhs):.4f} <= {lhs:.4f} <= {float(rhs):.4f}" + lines.append(line) + + with open(filepath, "w", encoding="utf-8") as f: + f.write("\n".join(lines) + "\n") \ No newline at end of file diff --git a/main/tableau.py b/main/tableau.py index 2e6dd7f..e0ce7c5 100644 --- a/main/tableau.py +++ b/main/tableau.py @@ -1,62 +1,108 @@ class Tableau: - def __init__(self, A, b, c, objective_type): + def __init__(self, A, b, c): """ - A = constraints matrix - b = independent terms - c = coefficients of the objective function - objective_type = "Max" or "Min" + A = constraints matrix (lista de listas com coeficientes) + b = independent terms (lista com RHS das restrições) + c = coefficients of the objective function (lista) """ - self.A = A self.b = b self.c = c - self.objective_type = objective_type self.num_constraints = len(A) self.num_vars = len(c) self.var_names = [f"x{i+1}" for i in range(self.num_vars)] + + self.slack_vars = [] + self.artificial_vars = [] + + self.basis = [] + + self.tableau = [] - self.base_vars = [f"w{i+1}" for i in range(self.num_constraints)] + def add_slack_variable(self, name=None): + if name is None: + name = f"w{len(self.slack_vars) + 1}" + self.slack_vars.append(name) + return name - self.build_tableau() + def add_artificial_variable(self, name=None): + if name is None: + name = f"a{len(self.artificial_vars) + 1}" + self.artificial_vars.append(name) + return name - def build_tableau(self): - n = self.num_vars + def build_tableau(self, slack_indices, artificial_indices=None, M=1000000, slack_types=None): + """ + Constrói o tableau do simplex. + + Args: + slack_indices: index list of constraints that receive slack variables + artificial_indices: index list of constraints that receive artificial variables + M: Big M penalty value for artificial variables + slack_types: dictionary {constraint_index: coef} where coef is 1 (<=) or -1 (>=) + """ m = self.num_constraints + n = self.num_vars + num_slack = len(slack_indices) if slack_indices else 0 + num_artificial = len(artificial_indices) if artificial_indices else 0 + num_cols = n + num_slack + num_artificial + 1 + self.tableau = [[0.0] * num_cols for _ in range(m + 1)] - self.tableau = [] for i in range(m): - row = self.A[i] + [0] * m + [self.b[i]] - row[n + i] = 1 - self.tableau.append(row) + for j in range(n): + self.tableau[i][j] = float(self.A[i][j]) + self.tableau[i][-1] = float(self.b[i]) + + base = [None] * m + + slack_col = n + for idx in (slack_indices or []): + var_name = self.add_slack_variable() + coef = 1.0 if slack_types is None else slack_types.get(idx, 1.0) + self.tableau[idx][slack_col] = coef + if coef == 1.0: + base[idx] = var_name + slack_col += 1 + + artificial_col = n + num_slack + artificial_col_indices = [] + for idx in (artificial_indices or []): + var_name = self.add_artificial_variable() + self.tableau[idx][artificial_col] = 1.0 + base[idx] = var_name + artificial_col_indices.append(artificial_col) + artificial_col += 1 + + for j in range(n): + self.tableau[-1][j] = -float(self.c[j]) + + for col_idx in artificial_col_indices: + self.tableau[-1][col_idx] = M + if artificial_indices: + for idx in artificial_indices: + for j in range(num_cols): + self.tableau[-1][j] -= M * self.tableau[idx][j] + + self.basis = base - if self.objective_type == "Max": - obj = [-ci for ci in self.c] - else: # Min - obj = self.c[:] + def get_all_var_names(self): + return self.var_names + self.slack_vars + self.artificial_vars - obj = obj + [0] * m + [0] - self.tableau.append(obj) + def format_tableau(self, iteration=0): + all_vars = self.get_all_var_names() + cabecalho = ["VB"] + all_vars + ["b"] + largura = 10 + lines = [] + lines.append(f"=== Iteracao: {iteration} ===") + lines.append(f"{cabecalho[0]:>15}" + "".join(f"{el:>{largura}}" for el in cabecalho[1:])) + for i, var in enumerate(self.basis): + row = f"{(var or ''):>15}" + "".join(f"{v:>{largura}.2f}" for v in self.tableau[i]) + lines.append(row) + obj = f"{'Z':>15}" + "".join(f"{v:>{largura}.2f}" for v in self.tableau[-1]) + lines.append(obj) + return "\n".join(lines) + "\n" def print_tableau(self, iteration=0): - Cabecalho = ["VB", "-Z"] + self.var_names + self.base_vars + ["b"] - largura = 6 - - print(f"=== Iteracao: {iteration} ===") - print(f"{Cabecalho[0]:>15}", end="") - for elemento in Cabecalho[1:]: - print(f"{elemento:>{largura}}", end="") - print() - - for i, var in enumerate(self.base_vars): - print(f"{var:>15}", end="") - for valor in self.tableau[i]: - print(f"{valor:>{largura}.2f}", end="") - print() - - print(f"{'-Z':>15}", end="") - for valor in self.tableau[-1]: - print(f"{valor:>{largura}.2f}", end="") - print() - print() + print(self.format_tableau(iteration), end="") \ No newline at end of file diff --git a/main/temp.py b/main/temp.py deleted file mode 100644 index 20a8ccb..0000000 --- a/main/temp.py +++ /dev/null @@ -1,121 +0,0 @@ -# Método Simplex para Problemas Básicos - -def Ler_Modelo(): - - """ - Max Z = 3*x1 + 2*x2 - s.a - [R1] 2*x1 + 1*x2 = 6 - [R2] 3*x1 + 2*x2 <= 12 - x1, x2 < 0 - """ - c = [3, 2] - A = [[2, 1], - [3, 2]] - b = [6, 12] - VD = ['x1', 'x2'] - - return c, A, b, VD - -def Imprimir_Tableau(Base, Iter, Variaveis, Tableau): - - Cabecalho = ['VB', '-Z'] + Variaveis + ['b'] - Largura = 6 #para as colunas numéricas - - # Impressão - print(f'=== Iteracao: {Iter} ===') - print(f'{Cabecalho[0]:>15}', end="") - for elemento in Cabecalho[1:]: - print(f'{elemento:>{Largura}}', end="") - print() - - # Impressão das Variáveis Básicas - for i, var in enumerate(Base): - print(f'{var:>15}', end="") - for valor in Tableau[i]: - print(f'{valor:>{Largura}.2f}', end="") - print() - - # Impressão da FO - print(f'{"-Z":>15}', end="") - for valor in Tableau[-1]: - print(f'{valor:>{Largura}.2f}', end="") - print() - -def Metodo_Simplex(c, A, b, VD): - - # Contando VDs e Restrições - m = len(A) # Número de Restrições - n = len(c) # Número de VDs - - # Coeficiente das Restrições - Tableau = [] - for i in range(m): - linha = [0] + A[i][:] + [0]*m + [b[i]] - linha[n + i + 1] = 1 - Tableau.append(linha) - - # Coeficientes da FO - linha_FO = [1] + [-j for j in c] + [0]*m + [0] - Tableau.append(linha_FO) - - # Nomeando as variáveis de folga - Folgas = [f'w{i+1}' for i in range(m)] - VD_Folgas = VD + Folgas - Base = Folgas[:] - - Iter = 0 - Imprimir_Tableau(Base, Iter, VD_Folgas, Tableau) - print() - - # Loop Principal - while True: - Z_Linha = Tableau[-1][1:-1] # última linha do tableau, da primeira até a última coluna - Menor_Coef = min(Z_Linha) # Captura o coeficiente mais negativo - - if Menor_Coef >= 0: # Critério de Parada - print('\nSolução Ótima Encontrada') - break - - # Identificando a Variável que Entrará na Base - Coluna_Pivo = Z_Linha.index(Menor_Coef) + 1 - - # Identificando a Variável que Sairá da Base - Vetor_Bloqueio = [] - for i in range(m): - if Tableau[i][Coluna_Pivo] > 0: - Valor_Bloq = Tableau[i][-1] / Tableau[i][Coluna_Pivo] - else: - Valor_Bloq = float("inf") - Vetor_Bloqueio.append(Valor_Bloq) - Linha_Pivo = Vetor_Bloqueio.index(min(Vetor_Bloqueio)) - - # Identificando Solução Ilimitada - if min(Vetor_Bloqueio) == float("inf"): - print('\nSolução Ilimitada') - return - - # Normalização da Linha Pivô - Pivo = Tableau[Linha_Pivo][Coluna_Pivo] - for j in range(len(Tableau[Linha_Pivo])): - Tableau[Linha_Pivo][j] = (Tableau[Linha_Pivo][j] / Pivo) - - # Zerando as colunas acima e abaixo da linha pivô (escalonamento de Gauss) - for i in range(len(Tableau)): - if i != Linha_Pivo: - Fator = Tableau[i][Coluna_Pivo] - for j in range(len(Tableau[1])): - Tableau[i][j] = Tableau[i][j] - Fator*Tableau[Linha_Pivo][j] - - # Atualizando a Base - Base[Linha_Pivo] = VD_Folgas[Coluna_Pivo - 1] - - Iter = Iter + 1 - Imprimir_Tableau(Base, Iter, VD_Folgas, Tableau) - print() - - - -if __name__ == "__main__": - c, A, b, VD = Ler_Modelo() - Metodo_Simplex(c, A, b, VD) \ No newline at end of file