Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
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.
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
A Julia interface to the Gurobi Optimizer
Branch-and-Price-and-Cut in Julia
A JuMP-based Nonlinear Integer Program Solver
A Julia interface to the CPLEX solver
A solver for mixed-integer convex optimization
Julia interface to SCIP solver
A Julia interface to the Coin-OR Branch and Cut solver (CBC)
A Julia interface to the Artelys Knitro solver
A Julia interface to the FICO Xpress Optimization suite
A Julia/JuMP Package for Optimal Quantum Circuit Design
A JuMP-based library of Non-Linear and Mixed-Integer Non-Linear Programs
Data-driven decision making under uncertainty using matrices
Extension of JuMP to model decomposable mathematical programs (using Benders or Dantzig-Wolfe decomposition paradigm)
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Benders decomposition to solve mixed integer linear programming, especially stochastic programming in seconds!
A Julia interface to the BARON mixed-integer nonlinear programming solver
FPBH: A Feasibility Pump based Heuristic for Multi-objective Mixed Integer Linear Programming
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