optimization-or

What problem does it solve?

This Skill helps you model and solve optimization problems (LP, QP, and MILP) and interpret results through dual variables, sensitivity analysis, and solver selection.

Core Features & Use Cases

• LP/QP/MILP Modeling: Formulate resource allocation, portfolio optimization, and integer decision problems using cvxpy with practical examples.
• Solver Options & Interfaces: Use open-source solvers (e.g., GLPK, ECOS, SCS) and integrate commercial Gurobi when available; also solve constraint satisfaction via OR-Tools CP-SAT and large-scale LP/MILP via OR-Tools pywraplp.
• Sensitivity & Decision Intelligence: Extract shadow prices (dual variables), reduced-cost style insights, and run practical sensitivity reasoning to understand how solutions change when constraints or targets shift.

Use Case: Optimize a production plan with limited labor/material/machine capacity, then read the shadow prices to quantify the marginal value of additional resources.

Quick Start

Ask your AI to formulate a production planning LP from your profit vector, resource-consumption matrix, and capacity limits, solve it with cvxpy, and return the optimal allocation plus the shadow prices for each resource.