linear-programming

What problem does it solve?

This Skill provides a solver for linear optimization problems: maximize or minimize a linear objective subject to linear constraints, enabling efficient decision making in resource allocation, planning, and scheduling tasks.

Core Features & Use Cases

• Simplex, interior-point, and graphical solutions for linear programs.
• Standard form support: handle A_ub, A_eq, b_ub, b_eq, and variable bounds.
• Use cases: resource allocation, production planning, transportation optimization, diet problems, and portfolio optimization.

Quick Start

Use the linear-programming skill with SciPy to solve a small example.

Python (SciPy): from scipy.optimize import linprog

Minimize: c^T x (maximize by negating c)

c = [-1, -2] A_ub = [[1, 1], [2, 1]] b_ub = [4, 5] res = linprog(c, A_ub=A_ub, b_ub=b_ub, method='highs') print('Optimal value:', -res.fun) print('Optimal solution:', res.x)