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.
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