Gurobi Optimization Masterclass

Learn LP, IP, MIP and Quadratic Programming with Gurobi, advanced modeling techniques, and hands-on Python examples.

What you'll learn

✓ Formulate and solve Linear Programming (LP) models using Gurobi and Python
✓ Model and optimize Integer Programming (IP) and Mixed-Integer Programming (MIP) problems
✓ Build and solve Quadratic Programming (QP) models, including convex quadratic objectives
✓ Apply advanced modeling techniques, logical constraints, and efficient formulations

Requirements

● Basic knowledge of Python programming (variables, loops, functions)
● Fundamental understanding of linear algebra (vectors, matrices)
● Familiarity with basic calculus concepts is helpful but not mandatory
● A computer capable of running Python and installing the Gurobi Optimizer
● Gurobi installation (academic or commercial license) before starting the hands-on sections

Description

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.

Who this course is for

■ Engineers and analysts working on optimization and decision-making problems
■ Data scientists who want to integrate mathematical optimization into their workflows
■ Operations research students and researchers learning Gurobi in practice
■ Python developers interested in advanced optimization modeling
■ Professionals in logistics, finance, manufacturing, energy, and supply chain optimization

Homepage

https://www.udemy.com/course/gurobi-optimization-masterclass

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