mixed-integer-programming

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

Linear optimization software

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

NVIDIA cuOpt examples for decision optimization

Represent trained machine learning models as Pyomo optimization formulations

Efficient modeling interface for mathematical optimization in Python

A Julia/JuMP-based Global Optimization Solver for Non-convex Programs

A Julia interface to the Gurobi Optimizer

General optimization (LP, MIP, QP, continuous and discrete optimization etc.) using Python

Branch-and-Price-and-Cut in Julia

A JuMP-based Nonlinear Integer Program Solver

Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

A Julia interface to the CPLEX solver

A solver for mixed-integer convex optimization

Certifiable Outlier-Robust Geometric Perception

Julia interface to SCIP solver

Hands-on course about linear programming and mathematical optimization.

Humble 3D knapsack / bin packing solver

BCP-MAPF – branch-and-cut-and-price for multi-agent path finding

Solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) using Mixed Integer Linear Programming (MILP) in Python with the Gurobi API.