Applied Optimization with Python and Gurobi

Optimization, Modeling, Gurobipy, Python
What you'll learn

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Master Smart Problem Formulation: Learn to transform complex financial and industrial challenges into precise mathematical models.
Pro Coding with Python & Gurobi: Build step-by-step optimization pipelines integrated smoothly with Excel and Pandas.
Harness AI for Rapid Development: Leverage the AI-powered Cursor IDE to accelerate your coding, debug instantly, and visualize solutions.
Solve Real-World Industry Projects: Master high-value applications like supply chain logistics, scheduling, and machine learning integration.
Requirements

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Basic Programming Skills (especially Python) Fundamental Concepts of Introductory Optimization Target Audience: Students of Industrial Engineering, Applied Mathematics, Management, Computer Engineering, and any student taking an optimization course Operations Research (OR) and Industrial Engineering Professionals Data Science Specialists Web and Software Developers
Description

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"This course contains the use of artificial intelligence."
This hands-on course teachesapplied optimization usingPython and theGurobi solver, with a strong focus on practical modeling and AI-assisted development in theCursor IDE.
Core topics include
•Linear Programming - production problems, duality, shadow prices, Simplex method, reduced costs, and slack variables.
•Integer & Binary Programming - BigM method, MIPGap, solution control, and general constraints (absolute value, min/max, indicator).
•Assignment & Network Problems - assignment problem logic, unimodularity, shortest path, and minimum spanning tree.
•NonLinear Programming - quadratic programming (QP), quadratically constrained programming (QCP), piecewise linear functions, and a warehouse location project.
•Multiobjective optimization - Pareto optimality, weighted sum, lexicographic methods, and portfolio case study.
•Advanced techniques - Irreducible Inconsistent Subsystem (IIS), multiscenario analysis, TSP with subtour elimination, lazy constraints, solution pool, warm starts (MIPStart), and tuning MIPFocus.
•Data integration - connecting Gurobi to Excel and Pandas, matrix-based modeling with addMVar, and performance tuning (CPU threads, TimeLimit).
•Machine learning connections - exact clustering and feature selection using MILP.
•Fun challenges - Sudoku, Magic Square, NQueens, Jealous Husbands, and an AI-driven coding challenge.
Target audience: Students of industrial engineering, applied math, computer science, and management; operations research professionals; data scientists; and developers.
Prerequisites: Basic Python and introductory optimization knowledge.
Goals: Master problem formulation, build production-ready pipelines, use AI for rapid development, and solve real-world logistics, scheduling, and ML integration projects.
Who this course is for

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• Students of Industrial Engineering, Applied Mathematics, Management, Computer Engineering, and any student taking an optimization course • Operations Research (OR) and Industrial Engineering Professionals • Data Science Specialists • Web and Software Developers
Homepage

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