Find your next solver

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

Welcome to AMPL (A Mathematical Programming Language), a powerful and intuitive modeling language designed specifically for large-scale optimization problems. If you are new to modeling languages or AMPL, this page will guide you through what AMPL is, its benefits, and why it stands out in the landscape of programming and modeling languages.

Course about basic modeling for discrete optimization with MiniZinc

CVXPY is an open source Python-embedded modeling language for convex optimization problems. It lets you express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers.

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

Discrete Optimization Talks (DOTs) is a virtual seminar series from the Mixed Integer Programming Society (MIPS), a technical section of the Mathematical Optimization Society (MOS). Topics of interest are theoretical, computational, and applied aspects of integer and combinatorial optimization. The typical format of each session is two 30-minute talks.

GAMS is a modeling system for mathematical optimization that lets users formulate complex problems in a high-level language and run them with different commercial and open-source solvers. All solvers are packaged in a single distribution, making it easy to switch between them and giving customers one point of contact for licensing and support. It also has a Python interface -GAMSPy- for building and solving mathematical optimization models using the GAMS technology stack. It keeps the modeling concepts of GAMS (sets, parameters, variables, equations) but exposes them as Python classes and functions. This allows modelers to stay in the Python ecosystem while using mature GAMS solvers and workflows.

Open source software suite for optimization, tuned for tackling the world's toughest problems in vehicle routing, flows, integer and linear programming, and constraint programming.

It features conversations between students, faculty and professionals around OR-related topics

JuMP is a domain-specific modeling language for mathematical optimization embedded in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs.

MiniZinc is a high-level constraint modelling language that allows you to easily express and solve discrete optimisation problems.

An extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). MIPLearn uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers such as CPLEX, Gurobi or XPRESS.

Thought leadership from the MIT Center for Transportation & Logistics.

The Network consists of six academic and nine industrial participants from five EU countries, USA and Chile. The participants combine strong and complementary expertise, within industry sectors ranging from energy, retailing, insurance to banking, as well as national statistical offices.

Discussions around optimization, interviewing academics in the field of Operations Research and Industrial Engineering

Optlang is a Python package implementing a modeling language for solving mathematical optimization problems, i.e. maximizing or minimizing an objective function over a set of variables subject to a number of constraints. Optlang provides a common interface to a series of optimization tools, so different solver backends can be changed in a transparent way.

7-day free email course to kickstart in Operations Research.

An LP modeler written in Python

Python-based, open-source optimization modeling language with a diverse set of optimization capabilities.

PyOptInterface is an open-source Python library to provide a unified API to construct and solve optimization models with various optimizers.

The podcast from INFORMS that talks about different topics around Operations Research

Course about all things mathematical optimization: unconstrained, LP, NLP, MIP, Global optimization, Dynamic optimization, ML for optimization, and optimization under uncertainty.

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

"Subject to" offers a series of informal conversations with relevant figures in the fields of Operations Research, Combinatorial Optimization and Logistics, and they are hosted by Anand Subramanian, an Associate Professor at Universidade Federal da Paraíba (UFPB), Brazil

The Decision Intelligence Lab explores practical challenges of applying data science, analytics, and AI to drive real-world business outcomes. Hosted by Prof. Michael Watson (Nortwestern University) and Prof. Vijay Mehrotra (University of San Francisco) - both seasoned entrepreneurs, consultants, and researchers - this podcast delivers real-world insights for data professionals, business leaders, & anyone seeking to leverage data for smarter decision making. Each episode features leaders sharing how smarter decisions are reshaping business and technology.