Linear Program Solver

Linear Program Solver: Solve Optimization Problems

A linear program solver is a software tool used to solve optimization problems with linear constraints and objectives. It takes a linear program formulated with variables, constraints, and an objective, then uses algorithms like the simplex method to find the optimal solution.

What is Linear Program Solver?

A linear program (LP) solver is a software application designed to solve mathematical optimization problems characterized by linear objective functions and linear constraints. Linear programming is used across business, engineering, economics, and science to optimize resource allocation problems with multiple constraints.

LP solvers take as input a linear program formulation consisting of decision variables, linear constraints in the form of linear equations or inequalities, and a linear objective function to maximize or minimize. Mathematical optimization algorithms, typically variations on the simplex algorithm, are then applied to find the variable configuration that optimizes the objective while satisfying the constraints.

Outputs from linear programming solvers include the optimal objective value and the values of the decision variables at the optimum. Additional sensitivity analysis features may be provided to analyze how the optimal solution changes with modifications to parameters or constraints.

Well-known open source and commercial LP solvers include GNU Linear Programming Kit (GLPK), IBM ILOG CPLEX Optimization Studio, Gurobi, Microsoft Solver Foundation, among others. They may be used as software libraries called from other applications or via graphical user interfaces or command line interfaces.

Linear programming has widespread applications in operations research, logistics, scheduling, portfolio optimization, and other areas. LP solver software enables practitioners to quickly build and analyze LP problem formulations to support optimization-based decision making.

Linear Program Solver Features

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