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.

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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

It's been a long way, but we've finished it! In this lesson, we're going to summarize what we've learned during the course.

## Introduction

In the first chapter of the course, we learned what optimization is and how it influences our lives. We set the prerequisites to get the best result out of the course and learned about all the tools we'd use. Then we learned the general definition of an optimization problem, how to write an optimization problem in mathematical form, and solved some simple optimization problems using plots.

## Derivatives and gradients

Here we learned about derivatives in one and multiple dimensions, and how they can be used to solve optimization problems. We defined and calculated derivatives, gradients, and Hessians of functions. We learned our first exact optimization algorithms and used SymPy to implement them in Python.



It's been a long way, but we've finished it! In this lesson, we're going to summarize what we've learned during the course.

# Introduction

In the first chapter of the course, we learned what optimization is and how it influences our lives. We set the prerequisites to get the best result out of the course and learned about all the tools we'd use. Then we learned the general definition of an optimization problem, how to write an optimization problem in mathematical form, and solved some simple optimization problems using plots.

# Derivatives and gradients

Here we learned about derivatives in one and multiple dimensions, and how they can be used to solve optimization problems. We defined and calculated derivatives, gradients, and Hessians of functions. We learned our first exact optimization algorithms and used SymPy to implement them in Python.



Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Summary

Introduction

Derivatives and gradients