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 know the candidate points are those in which the gradient has all its entries equal to zero. Therefore, for each point, we do a test with the Hessian so we can determine if the point is a minimum, a maximum, or neither.

# Getting the candidate points with Python

Let's break the code into pieces. We're going to calculate the gradient first and the points that make it zero. The function will be $f(x, y, z) = x^2 + y^2 + z^2$.

Learn to solve problems using gradients and Hessians.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Solving Problems with the Gradient and the Hessian

Getting the candidate points with Python