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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 converges faster than other algorithms.

We don't need to know the derivative of the target function nor even the target function itself.

It returns the exact solution to the problem.

Which is an advantage of binary search?

A differentiable and convex target function

What do we need to solve a problem with gradient descent?

What do we need to solve a problem with gradient descent?

It's a first-order optimization algorithm.

It requires the target function to be differentiable.

Which property holds for Newton's method?

Which property holds for Newton’s method?

Quiz on Optimization Algorithms

Quiz on Optimization Algorithms

Practice what you've learned about optimization algorithms.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Quiz: First Optimization Algorithms