Understand how Mixed-Integer Programming (MIP) and Mixed Integer Linear Programming (MILP) enable advanced decision-making through mathematical optimization.

At the Gurobi Summit in Vienna, Danone shared how optimization helps them overcome uncertainties to minimize costs while meeting demand

At the 2025 Gurobi Summit in Vienna, Agora explored how optimization-powered analysis can lead the way to energy system resilience and cost efficiency

Explore highlights from a recent Gurobi academic webinar, which brought together three distinct perspectives on optimization education and practice.

Understand how GPUs can impact the performance of LP and MIP models.

Learn how optimization in supply chain management with Gurobi improves sourcing, production, inventory, and logistics decisions using powerful mathematical models.

Discover how optimization in transportation with Gurobi improves routing, mode choice, and capacity use while respecting real-world constraints and service goals.

Learn how different optimization methods, from linear to mixed-integer models, help solve real business problems and how Gurobi fits into your decision workflow.

Working at the intersection of statistics and optimization, Dr. Guerrero paves the way for innovation and inclusivity in STEM education.

Explore key highlights from Gurobi’s annual report, with insight into where optimization is driving the most impact.

Learn how a solver like Gurobi turns business constraints into optimal decisions for production, logistics, and workforce planning using linear programming.

Explore practical linear programming examples in manufacturing, logistics, and workforce planning, and see how Gurobi turns them into optimal decisions.

Prescriptive analytics examples show how optimization with Gurobi turns forecasts into concrete decisions for supply chain, workforce, pricing, and more.

Explore how Gurobi 13.0 delivers faster performance, greater flexibility, and scalable solutions for modern optimization challenges.

Learn how financial experts use Gurobi to design, test, and refine portfolio strategies that balance performance, risk, and real-world trading constraints.

Educators share how generative AI is reshaping teaching, learning, and research—and what students need to succeed in this new era.

From academia to humanitarian logistics, Dr. Bahar Kara has led an inspiring career “using good OR to do good.”

Gurobi customers can now solve their large-scale LPs using the PDHG algorithm on GPU hardware and evaluate potential performance benefits.

Explore the basics of linear programming with the simplex method, including how it works and when to use it.

A recent Gurobi webinar explores one of the toughest questions in optimization: When can I trust my algorithm?

Explore the latest in mathematical modeling and optimization for machine learning.

Using predictive analytics, optimization, and natural language technologies, an MBA student built Smart Roster to instantly generate high-performing lineups for fantasy baseball players.

See how we're shaping the future of optimization—one innovation at a time.

Explore our latest innovation for instant optimization support and expert guidance.

Learn how Gurobi’s advanced solver handles quadratic programming challenges across industries.

Explore how Gurobi helps you optimize complex problems—from supply chains to finance—with speed, accuracy, and industry-leading performance.

Optimization has a powerful story to tell—not just about the problems it has already solved, but about the future it can shape.

Browse our list of Gurobi tutorial resources to help you start solving quickly.

Learn how AI is reshaping the way we solve complex problems.

Discover how you can use Gurobi to optimize for multiple objectives.

Discover why mathematical optimization is becoming a must-have skill for data scientists, with insights from our annual report.

On-Demand: Optimization 202 for Data Scientists

Build your first optimization model in Python with Gurobi’s intuitive API and solve real-world problems—fast and optimally.

Academic research is not only advancing the field of optimization, but also solving real-world problems.

Discover how Gurobi's interactive Burrito Optimization Game makes a complex concept more accessible.

Discover how quadratic optimization works, common methods, and real-world use cases.

Explore optimization model basics, including common types of models, key components, and resources to help you start optimizing.

Explore how linear programming (ILP) powers real-world decision-making across industries—and how Gurobi helps solve these challenges efficiently.

Explore our latest benchmark results for new GPU-enabled algorithms for barrier and PDHG.

Gurobi applauds the open-sourcing of NVIDIA’s cuOpt PDLP to help explore faster, more efficient solutions for real-world challenges.

Solving the Knapsack Problem: A Classic Challenge in Optimization

Discover why optimization expertise is in high demand and learn how you can enhance your own skills.

Uncover LP model basics, from key components to common use cases.

Learn how Decision Intelligence Technology Is Shaping the Future of Business Decisions

Leverage the Power of Decision Intelligence for Effective, Data-Driven Success

Mixed Integer Programming (MIP) combines continuous and discrete variables to optimize complex problems

Beyond financial returns, decision intelligence technology drives real-world impact—from reducing waste and improving healthcare to enhancing sustainability.

Optimizing Routes with Advanced Mathematical Programming

Discover how optimization with linear programming works, its use cases, and real-world examples.

Learn how to use Python for linear programming, solve real-world optimization problems

Linear programming optimizes decision-making by solving problems involving linear objectives and constraints. Learn methods, applications, and tools for success.

Learn the fundamentals of linear optimization, its techniques, and real-world applications.

See how linear programming in operations research powers real-world decisions in production, logistics, and planning, and how Gurobi helps solve these models at scale.

Optimization can transform businesses—but how do you convince executives to invest? Discover practical steps to communicate its value and secure stakeholder support.

Discover how MIP technology can enhance portfolio optimization, empowering investors to process large datasets, detect patterns, and make better decisions that align with their long-term financial goals.

Discover how to defend your optimization and machine learning models by addressing key questions around transparency, interpretability, and explainability.

Discover the value of enhanced solver performance and how it can help you achieve better, faster solutions.

See the message below for an update on Gurobi's participation in the Mittelmann benchmarks.

Explore key trends across a widening field.

Discover how a chatbot can make modeling easier by quickly answering optimization questions.

Discover benchmark results from Gurobi’s recent tests of the NVIDIA Grace CPU.

Learn how Gurobi supports better enterprise integration through standardization, automation, and scalability.

A groundbreaking approach to a decades-old puzzle.

A Gurobi evaluation lays the groundwork for a successful Gurobi deployment.

Learn how you can streamline your software development lifecycle and increase operational efficiency with Gurobi.

The Nextmv Gurobi integration accelerates how you run, test, and deploy decision models running Gurobi with Nexmv’s DecisionOps platform.

Your step-by-step guide to integrating Gurobi with Snowflake.

Learn how you can use Snowsight to streamline the execution of Gurobi models within Snowflake.

Confidently bridge the gap between simplified prediction models and operational complexity.

How GenAI can make optimization tools more accessible and effective

Discover how Decision Lab and Gurobi teamed up to enhance the efficiency and effectiveness of army training programs.

Unlock the power of optimized decision-making with our free Udemy course.

Explore the different methods for solving nonlinear problems and learn how Gurobi can help you obtain better, faster solutions.

Building a career that spans borders and industries.

Embracing optimization as a structured, disciplined science, infused with creativity yet grounded in methodical best practices.

From optimizing multiple objectives to dealing with infeasibility, Gurobi’s advanced modeling tools are here to help.

Try our optimization application demos to see how quickly Gurobi solves complex real-world problems.

Enrich your understanding of mathematical optimization and its real-world applications.

Opti 202: Intermediate Mathematical Optimization for Data Scientists

Opti 301: Advanced Mathematical Optimization for Data Scientists

Bridging the language gap for non-technical decision-makers.

Drive greater supply chain efficiency and profitability, while building a stronger and more resilient supply chain for the future.

Discover how Python optimization with Gurobi empowers data scientists and operations researchers to solve complex real-world problems efficiently.

From interactive games to comprehensive guides, we've got you covered.

Optimal Solutions leverages advanced optimization techniques, resulting in increased throughput, enhanced reliability, and minimized disruptions.

In addition to focusing on resilience, cost efficiency remains a non-negotiable for supply chain executives.

Discover how mathematical optimization can be applied to reduce wildfire hazard in forest landscapes.

On-Demand Optimization 101 for Data Scientists

The Gurobi Python Modeling and Development Environment

Learn the basic building blocks needed for optimization modeling with Gurobi in Python.

Make the best possible decisions on how to design your supply chain networks to ensure long-term, end-to-end efficiency and profitability

The following software packages can be used together with Gurobi Optimizer. However, Gurobi does not offer support services for these third-party tools.

Learn how a linear programming solver works and explore real-world applications.

In this article, Dr. Greg Glockner discusses how to protect your business against massive disruptions in an ever-changing business environment.

In our recent webinar, Dr. Alireza Soroudi shares how to use optimization in several of today’s most pressing power scenarios.

See how this heat exchanger example translates into an optimization problem—so you can apply the same approach to familiar chemical engineering equations.

You will find mathematical optimization in many unexpected places in our day-to-day world, even in the art world.

Gurobi speaks with Dr. Alireza Soroudi, Assistant Professor at the University College Dublin, about the impact of mathematical optimization in the energy industry today.

mathematical optimization has established itself as an indispensable tool for handling supply chain planning in general and supply chain disruptions in particular.

How can mathematical optimization help electricity companies capitalize on the opportunities and conquer the challenges presented by the transition from fossil fuels to renewable resources?

How can mathematical optimization and other advanced analytics tools help us overcome the healthcare challenges caused by the COVID-19 pandemic? Read this Gurobi interview with Kimia Ghobadi – the John C. Malone Assistant Professor in Civil and Systems Engineering at Johns Hopkins University – to find out.

How can mathematical optimization technologies help telecom companies tackle the operational and financial challenges of the 5G era?

How can mathematical optimization help us address the Covid-19 vaccine distribution challenges that we are facing today? Read this new article on Forbes.com by Gurobi CEO Ed Rothberg to find out.

How did mathematical optimization help two American economists win The Nobel Prize in Economics? Read this new article on Forbes.com by Gurobi CEO Ed Rothberg to find out.

What are the key factors to consider when designing a deployment architecture for your mathematical optimization application? Read this new blog by Gurobi’s Kostja Siefen, PhD to find out.

How would our world be changed if we didn’t have mathematical optimization technologies? In this new blog, Gurobi CEO and Co-founder Edward Rothberg, PhD examines the impact and importance of mathematical optimization in the electrical power and shipping industries.

Mathematical optimization applications can deliver tremendous bottom-line impact and return on investment (ROI). However, one of the chief challenges that the “champions” of mathematical optimization implementation projects face is accurately calculating and effectively communicating the business value of their applications to various stakeholders in their organizations.

How will the introduction of new, cutting-edge hardware systems by Apple and AMD impact the performance of mathematical optimization technologies? Get ready for a significant speed boost, Gurobi CEO Ed Rothberg explains in our latest blog post.

Understand your business situation today, so you can react quickly to disruptions.

What are the steps your company needs to take to successfully implement a mathematical optimization solution? Read this blog by Gurobi’s Dr. Kostja Siefen to find out.

Ask yourself these four questions to find out.

Many of us are working from home these days, which can give us a chance to work on projects that we may not have had the time or space to focus on in our normal, hectic office environments. If you are a user of a mathematical optimization application, now may be an ideal time to rethink and refine your mathematical optimization model.

How can mathematical optimization technologies benefit your business?

Why are mathematical optimization technologies – which were first introduced over 70 years ago – still so important and relevant in our world today? How will these technologies develop in the years to come? Gurobi Co-Founder Robert Bixby, PhD answers these questions in this blog.

How can businesses and governments today handle the unprecedented challenges and disruption caused by the coronavirus pandemic? Mathematical optimization technologies can help.

In this blog, we highlight the keys to delivering excellent customer support for companies utilizing mathematical optimization solvers.

How can businesses and governments today cope with the sudden and severe supply chain disruption caused by the coronavirus outbreak? One essential technology is mathematical optimization.

In the second part of this blog series, Gurobi Co-Founder Robert Bixby, PhD takes us through the history of mathematical optimization technologies.

What would our world be like without mathematical optimization?

Discover how optimization came to be, from optimization software pioneer Robert Bixby, PhD.

Discover how to go from data to decisions, with mathematical optimization.

View this collection of resources for an introduction on how Optimization can help you as a Data Scientist.

View this collection of resources for an introduction on how Optimization can help you as a Data Scientist.

View this collection of resources for an introduction on how Optimization can help you as a Data Scientist.

Starting with Gurobi

Discover how to go from data to decisions, with mathematical optimization.

With solver-independent systems like AIMMS, AMPL, GAMS, and MPL, migrating is easy. Just install a license, set the solver to Gurobi, and set your parameters.

Migrate from CPLEX™ to Gurobi—from building the model and setting solver parameters, to computing and extracting the solution.

Benchmark your models with the world's fastest solver.

Learn why companies are switching to Gurobi for better performance, support, and licensing.

Discover why migrating from OPL™ and CPLEX™ to AMPL™ and Gurobi is a powerful solution.

This page covers migrating from Xpress™ to Gurobi and walks through building the model, setting solver parameters, and computing and extracting the solution.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

Learn how to apply Gurobi to real-world linear programming with Python and explore techniques for building efficient linear optimization Python models.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

This video tutorial takes you through the foundational principles of Mixed-Integer Linear Programming. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

Discover the power of linear programming through our step-by-step training series.

Mathematical programming is a three-step process that involves formulating real-world problems into mathematical models.

Learn how to formulate the "Furniture Factory Problem" as a linear programming problem.

Exploring key components of linear programming and introducing mixed integer programming.

Learn how to solve the furniture problem using graphical solutions.

Understanding the simplex method for solving linear programming problems.

LP Ch.06: Modeling and Solving Linear Programming Problems

LP Ch.07: Sensitivity Analysis of Linear Programming Problems

LP Ch.08: Multiple Optimal Solutions

LP Ch.10: Infeasible Linear Programming Problems

LP Ch.11: Linear Programming Overview - Further Considerations

LP Ch.09: Unbounded Linear Programming Problems

LP Ch.12: Duality in Linear Programming

LP Ch.14: Dual Simplex Method

LP Ch.13: Optimality Conditions

Optimizing Logistics Operations Across Your End-To-End Supply Chain

Measuring the Gurobi Impact

While the mathematical optimization field is more than 70 years old, many customers are still learning how to make the most of its capabilities. That’s why, at Gurobi, we have established the Gurobi Alliance partner network—a group of trusted partners who can support you in achieving your optimization goals.

Launching an application designed to help you make optimal business decisions can lead to game-changing breakthroughs—but it also has the potential to yield some disappointing results, especially if the underlying formulation is weak.

As the world continues to gather mountains of data, we need math-minded people to help us make sense of it all. Therefore, it’s no surprise that demand for mathematics professionals is on the rise — second only to healthcare.

Make optimized decisions based on your predicted future.

Unlock the full value of data and drive optimal decision-making processes.

While the two technologies share some similarities, they serve distinct purposes and have unique applications.

Become quantum-ready, while leveraging existing optimization technologies and preparing for future advancements.

In this article, we highlight the three main pathways to successful mathematical optimization implementations and discuss how you can determine which approach is right for you.

In this blog, Gurobi CEO Ed Rothberg discusses three main reasons why mathematical optimization is such a powerful and versatile AI problem-solving technology.

In this new blog, Gurobi Chief Operating Officer Duke Perrucci reflects back on his career and identifies three times when the companies he was working for missed out on opportunities for growth because they didn’t have a mathematical optimization tool to help them make data-driven, optimal decisions.

Why are more and more companies deciding to invest in and implement mathematical optimization technologies?

Unlock new possibilities and better address complex real-world problems.