Mathematical Optimization in Python :Using PuLP, Python-MIP

Name: Mathematical Optimization in Python :Using PuLP, Python-MIP
Rating: 4.3 (11 reviews)

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

Created byShunsuke Hata

Last updated 7/2024

English

English [Auto],

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)

Course content

4 sections • 32 lectures • 3h 17m total length

Introduction2:38
Explore mathematical optimization with Python using PuLP and Python-MIP, compare their features, and apply to production planning, knapsack, and traveling salesman problems to build practical skills.
What is Mathematical Optimization?4:10
PuLP and Python-MIP5:46

Basic Syntax of PuLP5:44
System of Linear Equations5:40
Simplifying previous code3:29
Production Planning Optimization ①9:09
Formulate production planning optimization as a linear program with PuLP, maximizing profit from x and y under materials A and B constraints to reveal the optimal solution x=4 and y=1.
Production Planning Optimization ②10:26
Maximize profit by determining production of X, Y, and Z under material inventories A=5, B=10, C=8, D=12 kilograms using PuLP in a production planning optimization in Python.
Production Planning Optimization ② with Integer Constraints5:45
Explore production planning optimization with integer constraints, showing how enforcing integer variables changes optimal quantities and profit, with an example producing x=2, y=4, z=1 units and a profit of 24.
Quick Guide to List Comprehensions (Reference)2:54
Learn how to use list comprehensions in Python to double each element, replacing a multi-line for loop with a concise one-liner.
Introduction to the Knapsack Problem1:33
Solve the knapsack problem as a classic linear programming task by selecting items within capacity to maximize value, using pulp's LP sum and LP dot for summation and dot products.
Knapsack Optimization Using lpSum10:40
Knapsack Optimization Using lpDot3:55
Explore knapsack optimization with lpDot in PuLP, replacing lengthy expressions with concise lpDot formulations using lists x, y, z and a, b, c, and compare with lpSum.
Quick Guide to Dictionary (Reference)6:39
Knapsack Problem with Larger Datasets17:33
Overview of the Traveling Salesman Problem (TSP)8:31
Solving Traveling Salesman Problem ①9:01
Solving Traveling Salesman Problem ②13:33
Solving Traveling Salesman Problem ③7:54
Solve the traveling salesman problem using PuLP and Python-MIP to obtain an optimal route with minimum cost 19, mapping nodes to cities and validating subtour elimination.

PuLP vs. Python-MIP2:03
Compare python mip and pulp, noting that python mip treats constraints and variables as objects and uses CBC, offering flexibility for advanced computations, while pulp suffices for standard problems.
Syntax Differences Between PuLP and Python-MIP3:34
System of Linear Equations2:09
Production Planning Optimization ①7:50
Production Planning Optimization ②8:35
Production Planning Optimization ② with Integer Constraints3:33
Knapsack Optimization7:27
Solve the knapsack problem by selecting items with binary decisions to maximize value under a capacity of 15, using PuLP and Python-MIP.
Knapsack Problem with Larger Datasets7:55
Traveling Salesman Problem8:27
Solve the traveling salesman problem using Python mip by defining binary variables and a minimize objective, adding constraints, and solving, with a comparison to pulp's function-based approach.

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 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

Mathematical Optimization in Python :Using PuLP, Python-MIP

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 13min

Development Environment Preparation4 lectures • 11min

PuLP Learning16 lectures • 2hr 2min

Python-MIP Learning9 lectures • 52min

Requirements

Description

Who this course is for: