Optimization with Python: Complete Pyomo Bootcamp A-Z

Optimization with Python: Complete Pyomo Bootcamp A-Z

English | MP4 | AVC 1280Ă—720 | AAC 44KHz 2ch | 37 lectures (9h 24m) | 3.15 GB

Learn How to Use CPLEX, IPOPT & COUENNE Solvers to Solve Linear & Non-Linear and Integer Programming Problems in Python

Mathematical Optimization is getting more and more popular in most quantitative disciplines, such as engineering, management, economics, and operations research. Furthermore, Python is one of the most famous programming languages that is getting more attention nowadays. Therefore, we decided to create a course for mastering the development of optimization problems in the Python environment. In this course, you will learn how to deal with various types of mathematical optimization problems as below:

  • Linear Programming (LP)
  • Mixed Integer Linear Programming (MILP)
  • Non-Linear Programming
  • Mixed Integer Non-Linear Programming

Since this course is designed for all levels (from beginner to advanced), we start from the beginning that you need to formulate a problem. Therefore, after finishing this course, you will be able to find and formulate decision variables, objective function, constraints and define your parameters. Moreover, you will learn how to develop the formulated model in the Python environment (using the Pyomo package).

Here are some of the important skills that you will learn when using Python in this course:

Defining Sets & Parameters of the optimization model
Expressing the objective function and constraints as Python function
Import and read data from an external source (CSV or Excel file)
Solve the optimization problem using various solvers such as CPLEX, IPOPT, COUENNE &, etc.

In this course, we solve simple to complex optimization problems from various disciplines such as engineering, production management, scheduling, transportation, supply chain, and … areas.

This course is structured based on 3 examples for each of the main mathematical programming sections. In the first two examples, you will learn how to deal with that type of specific problem. Then you will be asked to challenge yourself by developing the challenge problem into the Python environment. Nevertheless, even the challenge problem will be explained and solved with details.

What you’ll learn

  • Basic Concepts and Terms Related to Optimization
  • How to Formulate a Mathematical Problem
  • Linear Programming and Coding LP Problems in Python Using Pyomo
  • Mixed Integer Linear Programming (MILP) and Coding MILP Problems in Python Using Pyomo
  • Non-Linear Programming (NLP) and Coding NLP Problems in Python Using Pyomo
  • Mixed Integer Non-Linear Programming (MINLP) and Coding MINLP Problems in Pyhton Using Pyomo
Table of Contents

Introduction
1 Course Introduction
2 Course Content

Introduction to Mathematical Optimization
3 A Review on Optimization’s Important Concepts

Python Installation
4 Why Google Colab
5 Review on Colab Environment
6 Pyomo Installation

Linear Programming (LP)
7 Introduction to LP problems
8 Example1 Problem Formulation
9 Example 1 Model Development in Python
10 Example2 Problem Formulation
11 Example2 Model Development in Python
12 LP Challenge Problem
13 LP Challenge Solution in Python
14 List of Solvers

Mixed-Integer Linear Programming (MILP)
15 Introduction to Integer Programming
16 Example1 Problem Formulation
17 Example1 Model Development in Python
18 Example2 Problem Formulation
19 Example2 Model Development in Python
20 MILP Challenge Problem
21 MILP Challenge Solution in Python

Non-Linear Programming (NLP)
22 Introduction to Non-Linear Programming
23 Example1 Problem Formulation
24 Example1 Model Development in Python
25 Example2 Problem Formulation
26 Example2 Model Development in Python
27 NLP Challenge Problem
28 NLP Challenge Solution

Mixed-Integer Nonlinear Programming (MINLP)
29 Introduction to Mixed-Integer Non-Linear Programming
30 Example1 Problem Formulation
31 Example1 Model Development in Python
32 Example2 Problem Formulation
33 Example2 Model Development in Python
34 MINLP Challenge Problem
35 MINLP Challenge Solution

Conclusion
36 Review & Reading Suggestions

Bonus
37 Your Special Bonus

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