Mathematical Optimization in Python:Using PuLP, Python-MIP

Advanced optimization techniques are essential for finding optimal solutions to the increasingly complex operational and long-term planning tasks companies face today. With information changing rapidly, decision-making has become a challenging task. Therefore, professionals in this field are among the most valued in the market.

In this course, you will learn the necessary skills to solve problems by applying Mathematical Optimization using Linear Programming (LP). We will focus on two powerful Python libraries: PuLP and Python-MIP. What You'll Learn:Introduction to Mathematical OptimizationUsing PuLP and Python-MIP for optimization problemsDifferences and features of PuLP and Python-MIPPractical applications through various problems:The Knapsack ProblemThe Traveling Salesman Problem (TSP)Production Planning OptimizationThe following solvers and frameworks will be explored:Solvers: CBC (default solver for both PuLP and Python-MIP)Frameworks: PuLP and Python-MIPThe classes use examples created step by step, so we will build the algorithms together.

This hands-on approach ensures you can follow along and understand the process of creating and solving optimization models. ems. We will also provide an introduction to mathematical modeling, so you can start solving your problems immediately.

I hope this course can help you in your career. Enroll now and start your journey to mastering optimization with Python.