How We Gamified Mathematical Optimization Using Burritos
It was in the hopes of making the complex more accessible that my team at Gurobi Optimization decided to collaborate with top educators to develop our own web-based learning tool: the Burrito Optimization Game.
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Solving Complex Problems with Quadratic Optimization
Discover how quadratic optimization works, common methods, and real-world use cases.
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Optimization Models 101: Types, Tips, and Resources
Explore optimization model basics, including common types of models, key components, and resources to help you start optimizing.
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Gurobi Teams With NVIDIA to Advance First-Order Methods for Large-Scale Optimization
Gurobi applauds the open-sourcing of NVIDIA’s cuOpt PDLP to help explore faster, more efficient solutions for real-world challenges. 
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Using GPUs to Solve LPs: What’s in It for Me?
Explore our latest benchmark results for new GPU-enabled algorithms for barrier and PDHG.
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Solving the Knapsack Problem: A Classic Challenge in Optimization
Learn how optimization can solve the Knapsack Problem and address complex resource allocation challenges.
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Mission-Critical: Why Optimization Experts Are Today’s Business MVPs
Discover why optimization expertise is in high demand and learn how you can enhance your own skills.
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LP Model Basics: Key Components, Use Cases, and Tips
Uncover LP model basics, from key components to common use cases.
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Discover the Power of Decision Intelligence Technology with Gurobi
Optimize complex business decisions with AI-powered insights and advanced mathematical modeling.
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Unlocking Business Success with Decision Intelligence: How AI and Advanced Analytics Drive Smarter Decisions
Discover how decision intelligence leverages AI and analytics to optimize decision-making, enhance efficiency, and drive business success.
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Mixed Integer Programming with Python: Solutions for Complex Problems
Mixed Integer Programming (MIP) combines continuous and discrete variables to optimize complex problems. Explore its applications, techniques, and Python solutions here.
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