Optimization with Python: Complete Pyomo Bootcamp A-Z
Learn How to Use CPLEX, IPOPT & COUENNE Solvers to Solve Linear & Non-Linear and Integer Programming Problems in Python
Created by Navid Shirzadi | 9.5 hours on-demand video course
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. 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).
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
Recommended Course by Navid Shirzadi
Optimization with Genetic Algorithms: Hands-on Python [NEW COURSES]
PyTorch for Deep Learning Bootcamp: Zero to Mastery
