optimization-theory-expert

Formulates and solves optimization problems — defining decision variables, objective functions, and constraints — and selects appropriate solvers for LP, QP, NLP, or MIP.

Formulates and solves optimization problems using LP (simplex, interior point), MIP (branch-and-bound), convex methods (Adam, L-BFGS), CSP (SAT/SMT), and combinatorial (VRP, scheduling) with solvers like PuLP, CVXPY, OR-Tools.

Solve Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Quadratic Programming (QP, beta) with the Python API. Use when the user asks about optimization with linear or quadratic objectives, linear constraints, integer variables, scheduling, resource allocation, facility location, production planning, portfolio optimization, or least squares.