Udemy – Gurobi Optimization Masterclass

Master mathematical optimization with Gurobi in this comprehensive, hands-on course covering Linear Programming (LP), Integer Programming (IP), Mixed-Integer Programming (MIP), and Quadratic Programming (QP). Designed for engineers, data scientists, operations researchers, and analysts, this course provides both the theoretical foundations and practical modeling skills needed to solve real-world optimization problems efficiently.

You will start by building a strong understanding of linear programming models, objective functions, and constraints. Then, you will move into integer and mixed-integer programming, learning how to model discrete decisions, logical constraints, and combinatorial optimization problems. The course also introduces quadratic programming, including convex quadratic objectives and constraints, and explains when and how to use them in practice.

A major focus of this course is advanced modeling techniques in Gurobi, including efficient formulation strategies, performance tuning, solver parameters, and model debugging. You will learn how to translate business and engineering problems into scalable optimization models.

All concepts are reinforced with practical Python examples using the Gurobi Python API. You will implement models from scratch, analyze solver output, interpret results, and improve model performance. By the end of the course, you will be able to confidently build, solve, and optimize complex LP, IP, MIP, and QP models using Gurobi in Python for real-world applications.

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