Find your next solver

Course about advanced modeling for discrete optimization, covering topics such as debugging or predicates

Optimization Expert | Algorithm Design | Operations Research

An arcade-style TSP challenge game written for https://github.com/tunnuz/arcadetsp's 2018 Christmas Party, follow the in-game instructions.

Course about basic modeling for discrete optimization with MiniZinc

Learn supply chain principles through a visual simulation.

The CG:SHOP Challenge (Computational Geometry: Solving Hard Optimization Problems) is an annual competition dedicated to tackling specific, difficult geometric optimization problems. Originally established as a workshop at Computational Geometry Week (CG Week) in 2019, the Challenge provides a unique platform where the success of proposed solutions is measured based on computational performance on a set of carefully selected benchmark instances. This focus on empirical evaluation marks a shift from traditional computational geometry research, which often emphasizes theoretical approaches.

An introduction to algorithms and computational complexity to solve combinatorial optimization problems

A mathematical optimization solver for large-scale optimization problems. It is independently developed by Cardinal Operations, and it includes high-performance solvers for LP, MIP, SDP, (MI)SOCP, convex (MI)QP, convex (MI)QCP and exponential cone programming.

Customer success stories from Coupa including optimization problems

The IBM ILOG CPLEX Optimizer solves integer programming problems, very large[3] linear programming problems using either primal or dual variants of the simplex method or the barrier interior point method, convex and non-convex quadratic programming problems, and convex quadratically constrained problems (solved via second-order cone programming, or SOCP).

Achieve world-record speed on large-scale problems with millions of constraints and variables—saving time and reducing costs.

A game to better understand how CVRP works.

Instances for the CVRP challenge.

Challenges from the Center for Discrete Mathematics and Theoretical Computer Science

The HiGHS Discord server.

Journal covering algorithmic and applied areas of discrete mathematics

Course about discrete optimization covering a wide range of optimization techniques such as Constraint Programming, Local Search or Column Generation

The FICO Xpress optimizer is a commercial optimization solver for linear programming (LP), mixed integer linear programming (MILP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP), second-order cone programming (SOCP) and their mixed integer counterparts. Xpress includes a general purpose non-linear solver, Xpress NonLinear, including a successive linear programming algorithm (SLP, first-order method), and Artelys Knitro (second-order methods).

GCG is a generic decomposition solver for mixed-integer programs (MIPs). It automatically performs a Dantzig-Wolfe reformulation and runs a full-fledged branch-price-and-cut algorithm to solve it to optimality. Alternatively, GCG is able to automatically apply a Benders decomposition.

For solving large-scale linear programming (LP), mixed integer programming (MIP), and other related problems. It is a set of routines written in ANSI C and organized in the form of a callable library.

Gurobi Optimizer is a prescriptive analytics platform and a decision-making technology developed by Gurobi Optimization, LLC. The Gurobi Optimizer (often referred to as simply, “Gurobi”) is a solver, since it uses mathematical optimization to calculate the answer to a problem.

Blog with news from Gurobi, the company behind one of the most important solvers globally

Use cases from Gurobi

HiGHS is open-source software to solve linear programming (LP), mixed-integer programming (MIP), and convex quadratic programming (QP) models.

To solve a sparse symmetric system of linear equations.

Optimization software that leverages machine learning to provide unparalleled performance.

Reader in Mathematics at the University of Edinburgh

The 2026 Land-Doig MIP Competition is a computational competition hosted by the 2026 Mixed Integer Programming Workshop, offering an opportunity for teams to showcase their practical and computational prowess and fostering innovation in computational MIP. It is named in honor of Ailsa H. Land and Alison G. Harcourt (née Doig), who proposed the first LP-based branch-and-bound algorithm.

Interactive resources to learn Graph Theory.

LINGO includes a set of built-in solvers to tackle a wide variety of problems. Unlike many modeling packages, all of the LINGO solvers are directly linked to the modeling environment.

A Mixed Integer Linear Programming (MILP) solver.

Professor of operations research at RWTH Aachen University.

Assistant Professor of Transportation Engineering @ York University | Director of Interactive-OR | Amazon Visiting Academic @ Amazon

Teaching and writing about all things AI, operations, and his startup experience.

Thought leadership from the MIT Center for Transportation & Logistics.

A discussion about books focused on duality theory

Case studies from Optimal Dynamics

This book offers an up-to-date description of relaxation/approximation and decomposition techniques, demonstrating how their combined use efficiently solves large-scale optimization problems relevant to engineering, particularly in electrical, and industrial engineering, with a focus on energy.

An interesting book about Column Generation to tackle large-scale optimization problems

Customer success stories from ORTEC

Challenge from Parameterized Algorithms and Computational Experiments Challenge to bridge the gap between theory and practice of algorithms

President of Optisol, Scheduling Specialist for High-Variety, Order-Driven Production and Resource-Constrained Projects.

Instances for Quadratic Assignment Problems.

SBB is a GAMS solver for Mixed Integer Nonlinear Programming (MINLP) models. It is based on a combination of the standard Branch and Bound (B&B) method known from Mixed Integer Linear Programming and some of the standard NLP solvers already supported by GAMS. SBB can use all GAMS NLP solvers as subsolvers but it works best with NLP solvers that can utilize a near optimal point as a starting point like Conopt, Minos, and Snopt.SBB supports all types of discrete variables supported by GAMS, including Binary, Integer, Semicont, Semiint, Sos1, and Sos2.

SCIP is currently one of the fastest non-commercial solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). It is also a framework for constraint integer programming and branch-cut-and-price.

SHOT (Supporting Hyperplane Optimization Toolkit) is a deterministic solver for mixed-integer nonlinear programming problems (MINLPs). Originally, SHOT was intended for convex MINLP problems only, but now also has functionality to solve nonconvex MINLP problems as a heuristic method without providing any guarantees of global optimality. SHOT can solve certain nonconvex problem types to global optimality as well.

SNDlib is a library of data for Survivable fixed telecommunication Network Design. Its purpose is to provide data about realistic telecommunication networks (including traffic) available to the research community.

Course about solving discrete optimization problems with different techniques: Constraint Programming, Mixed Integer Programming and Local Search

Learn about cutting planes in an adictive way.

Mathematical Optimization Researcher, Teacher, and Manager

Another game with map visualizations for the TSP.

Solve the TSP in a visual way from the Productions and Operations Management (Universitat Wein).

TSPLIB is a library of sample instances for the TSP (and related problems) from various sources and of various types.

Vehicle Routing and Logistics work group from EURO

This web is dedicated to the study of VRP; we have compiled here a great deal of information about it for public access: technical reports, many different variants of the problem, alternative algorithms and techniques for solving it, some well-known instances of the problem, the best-so-far solutions for those instances, bibliography, related links…

Professor Emeritus, Princeton University/ Co-Founder, Optimal Dynamics, shares knowledge about sequential decision problems.