Gain insights into solving optimization problems in Python. Delve into essential mathematical tools, metaheuristic methods, and techniques for constrained optimization. Become proficient in efficient Python solutions.

Optimization with Python from zero to hero.png

cvxpy.tar.gz

cvxpy

Optimization theory seeks the best solution, which is pivotal for machine learning, cost-cutting in manufacturing, refining logistics, and boosting finance profits.

This course provides a detailed description of different optimization problems and the techniques used to solve them. You’ll begin with the formal definition of an optimization problem and an overview of essential mathematical tools: derivatives, gradients, and Hessian. With this knowledge, you’ll implement solutions for several optimization problems. You’ll also learn approximate metaheuristic population methods like particle swarm optimization and genetic algorithms, and solve constrained optimization problems using techniques like penalty methods and constraint simplification. Finally, you’ll solve linear programs and integer programs. 

By the end of this course, you’ll become proficient in formulating different kinds of problems as optimization problems and become skilled in the Python tools that efficiently solve these problems.

Mastering Optimization with Python

We hope you've dedicated time to try to solve the exercises. Please remember that this kind of practice will help you grasp the content faster and better.

In case you want to check if your answer is right or if you got stuck in some of those exercises, we'll solve all of them here. Let's get started!

# Exercise 1: The can of soda

Now we're going to solve, once and for all, the problem of the soda can. Last time, we needed to add a restriction because we didn't know how to calculate minima exactly.

To solve this problem, we can employ the code to calculate minima and maxima using the derivative, and substitute the function for the one that describes the surface of the can.

Learn the solutions to the exercises from the previous lesson.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Solutions

Exercise 1: The can of soda