Mathematical Optimization in Python :Using PuLP, Python-MIP

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1. Introduction

1. Introduction.mp4

02:38

2. What is Mathematical Optimization.mp4

04:10

3. PuLP and Python-MIP.mp4

05:46

2. Development Environment Preparation

1. Installing Python.mp4

03:22

2. Installing Visual Studio Code.mp4

02:33

3. Installing VScode Extension.mp4

02:35

4. How to create virtual env.mp4

02:45

3. PuLP Learning

1. Basic Syntax of PuLP.mp4

05:44

2. System of Linear Equations.mp4

05:40

3. Simplifying previous code.mp4

03:29

4. Production Planning Optimization.mp4

09:09

5. Production Planning Optimization.mp4

10:26

6. Production Planning Optimization with Integer Constraints.mp4

05:45

7. Quick Guide to List Comprehensions Reference.mp4

02:54

8. Introduction to the Knapsack Problem.mp4

01:33

9. Knapsack Optimization Using lpSum.mp4

10:40

10. Knapsack Optimization Using lpDot.mp4

03:55

11. Quick Guide to Dictionary Reference.mp4

06:39

12. Knapsack Problem with Larger Datasets.mp4

17:33

13. Overview of the Traveling Salesman Problem TSP.mp4

08:31

14. Solving Traveling Salesman Problem.mp4

09:01

15. Solving Traveling Salesman Problem.mp4

13:33

16. Solving Traveling Salesman Problem.mp4

07:54

4. Python-MIP Learning

1. PuLP vs. Python-MIP.mp4

02:03

2. Syntax Differences Between PuLP and Python-MIP.mp4

03:34

3. System of Linear Equations.mp4

02:09

4. Production Planning Optimization .mp4

07:50

5. Production Planning Optimization.mp4

08:35

6. Production Planning Optimization with Integer Constraints.mp4

03:33

7. Knapsack Optimization.mp4

07:27

8. Knapsack Problem with Larger Datasets.mp4

07:55

9. Traveling Salesman Problem.mp4

08:27

Description

A Practical Approach to Solving Optimization Problems: Learn PuLP and Python-MIP Syntax and Features

What You'll Learn?

Basic Usage of Python Libraries such as PuLP and Python-MIP
Differences and Features of PuLP and Python-MIP
Fundamentals of Mathematical Optimization including Linear Programming and its Applications
Classic Problems and Solutions in Mathematical Optimization: Production Planning, Knapsack Problem and Traveling Salesman Problem (TSP)

Who is this for?

Individuals eager to gain expertise in leveraging Mathematical Optimization, including Linear Programming, for practical business applications

Those striving to attain proficiency in the fundamental usage of Python libraries like PuLP and python-mip

What You Need to Know?

How to use list and dictionary types in Python (not necessarily required)

More details

Description
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 Optimization
Using PuLP and Python-MIP for optimization problems
Differences and features of PuLP and Python-MIP
Practical applications through various problems:
The Knapsack Problem
The Traveling Salesman Problem (TSP)
Production Planning Optimization
The following solvers and frameworks will be explored:
Solvers: CBC (default solver for both PuLP and Python-MIP)
Frameworks: PuLP and Python-MIP

The 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!
Who this course is for:
Individuals eager to gain expertise in leveraging Mathematical Optimization, including Linear Programming, for practical business applications
Those striving to attain proficiency in the fundamental usage of Python libraries like PuLP and python-mip

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