Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
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Updated
May 7, 2024 - Julia
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Linear optimization software
General optimization (LP, MIP, QP, continuous and discrete optimization etc.) using Python
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
Branch-and-Price-and-Cut in Julia
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
Python implementation to solve Vehicle Routing problem & Master Production Scheduling in Supply Chain Analytics & Design.
BCP-MAPF – branch-and-cut-and-price for multi-agent path finding
An open-source parallel optimization solver for structured mixed-integer programming
Julia interface to SCIP solver
A JuMP-based Nonlinear Integer Program Solver
A predictive model to help Uber drivers make more money
A solver for mixed-integer convex optimization
Solving a Capacitated Vehicle Routing Problem with time windows constraints (CVRPTW) with Mixed Integer Linear Programming (MILP) in python-gurobi API.
Minotaur Toolkit for Mixed-Integer Nonlinear Optimization
Python API for AMPL
Humble 3D knapsack / bin packing solver
Joint placement and scaling of bidirectional network services with stateful virtual or physical network functions
Hands-on course about linear programming and mathematical optimization.
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