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.
Represent trained machine learning models as Pyomo optimization formulations
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
General optimization (LP, MIP, QP, continuous and discrete optimization etc.) using Python
Efficient modeling interface for mathematical optimization in Python
A Julia interface to the Gurobi Optimizer
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
A Julia interface to the Coin-OR Branch and Cut solver (CBC)
An open-source parallel optimization solver for structured mixed-integer programming
BCP-MAPF – branch-and-cut-and-price for multi-agent path finding
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