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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'll implement the binary search algorithm and solve a practical problem with it. Let's begin getting the idea behind the implementation, step by step.

# Implementing binary search

The first decision to make is: Where do we begin the search? In this case, we don't start from one place but two, simultaneously. We need two starting points. The only condition is that one should be in the invalid region and the other in the valid one. We'll call them points $a$ and $b$, respectively.

Learn to implement the binary search algorithm and solve an example.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Binary Search Implementation

Implementing binary search