gspl

gspl (short for Go Simplex Programming Library, pronounced gospel) is a lightweight solver for linear programming problems, built in Go using the revised simplex method. It emphasizes clarity, performance, and seamless integration into Go applications.

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Features

• Solves optimal, unbounded, infeasible, and degenerate linear problems.
• Implements an efficient revised simplex algorithm.
• Clean and idiomatic API for modeling and solving LPs.
• Focused on numerical stability and usability.
• Performs basic branch-and-bound techniques for pure integer problems.
• Provides native multi-objective optimization support.
• Builtin bindings for C and Python (coming soon).

gspl is ideal for embedding linear optimization into Go-based software.

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Installation

Ensure you have Go installed, then run:

go get github.com/chriso345/gspl

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Usage

Full examples are available in the gspl_examples_test.go file and the examples/ directory.

Variables

Create decision variables as a slice:

variables := []lp.LpVariable{
  lp.NewVariable("x1"),
  lp.NewVariable("x2"),
}

You can access and pass them by value or take their address when needed:

x1 := &variables[0]

Forcing integer constraints is done when creating the variable:

variables := []lp.LpVariable{
  lp.NewVariable("x1", lp.LpCategoryInteger),
  lp.NewVariable("x2", lp.LpCategoryInteger),
}

Objective Function

Build the objective using terms:

objective := lp.NewExpression([]lp.LpTerm{
  lp.NewTerm(5, variables[0]),
  lp.NewTerm(3, variables[1]),
})

Add it to the LP:

prog := lp.NewLinearProgram("My LP", variables)
prog.AddObjective(lp.LpMaximise, objective)

Constraints

Each constraint uses an expression, a comparison type, and a right-hand side:

prog.AddConstraint(lp.NewExpression([]lp.LpTerm{
  lp.NewTerm(2, variables[0]),
  lp.NewTerm(3, variables[1]),
}), lp.LpConstraintLE, 10)

Constraint types:

• gspl.LpConstraintLE - less than or equal
• gspl.LpConstraintGE - greater than or equal
• gspl.LpConstraintEQ - equality

Solving

solution, err := solver.Solve(&prog)
if err != nil {
  // handle error
}
fmt.Printf("obj=%.2f, x=%.2f\n", solution.ObjectiveValue, solution.PrimalSolution.AtVec(0))

This solves the model and prints variable values and the objective result.

Multi-Objective Support

gspl provides native multi-objective optimization capabilities via the mop package.

x := lp.NewVariable("x")
y := lp.NewVariable("y")
p := lp.NewLinearProgram("Pareto", []lp.LpVariable{x, y})
p.AddObjective(lp.LpMaximise, lp.NewExpression([]lp.LpTerm{lp.NewTerm(1, x)}))
p.AddObjective(lp.LpMaximise, lp.NewExpression([]lp.LpTerm{lp.NewTerm(1, y)}))
p.AddConstraint(lp.NewExpression([]lp.LpTerm{lp.NewTerm(1, x), lp.NewTerm(1, y)}), lp.LpConstraintLE, 10)

sols, err := mop.SolvePareto(&p, mop.ParetoWeightedSum, mop.WithWeightedSums([][]float64{{1, 0}, {0, 1}}))
if err != nil {
  fmt.Println("err", err)
  return
}

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License

Licensed under the MIT License. See the LICENSE file for more details.