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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

We've reached the end of the course, but what has been the most important thing we learned? Maybe the algorithms? The optimization theory? The Python libraries?

# The optimization mindset

Of course, that's all amazing knowledge that opens doors for us and allows us to solve a wide variety of problems. But the most important thing we learned is the optimization mindset. This is by far the greatest knowledge we can extract from this course: the ability to observe the reality around us and view optimization problems solved or unsolved in rich detail, and the ability to express problems of real life as optimization problems that can be solved with the methods we learned in this course.

This ability is a gift that allows us to understand our reality better and solve problems from a much better perspective. This is, over any algorithm or library, the ability that allows us to solve problems that others can't solve.



Reflect on optimization, what we’ve learned, and what’s left to learn.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Conclusion

The optimization mindset