+- Softwarez.Info - Software's World! (https://softwarez.info)
+-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone)
+--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials)
+--- Thread: Mathematical Optimization in Python - Using PuLP, Python-MIP (/Thread-Mathematical-Optimization-in-Python-Using-PuLP-Python-MIP--503479)
Mathematical Optimization in Python - Using PuLP, Python-MIP - OneDDL - 08-09-2024
![[Image: 1434da327e1c70605f3f71d818216c14.jpeg]](https://i123.fastpic.org/big/2024/0808/14/1434da327e1c70605f3f71d818216c14.jpeg)
Free Download Mathematical Optimization in Python - Using PuLP, Python-MIP
Published 7/2024
Created by Shunsuke Hata
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 32 Lectures ( 3h 17m ) | Size: 1.45 GB
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)
Requirements:
How to use list and dictionary types in Python (not necessarily required)
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 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!
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
Homepage
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
No Password - Links are Interchangeable