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

They provide good solutions to problems that can't be solved with exact or approximated methods.

The runtime to get solutions to very hard problems is commonly less than the time that it takes for an exact algorithm to get those solutions.

They provide more accuracy in the approximations of the solution compared with approximated algorithms.

They are global optimizers that can work with a large range of target functions.

**(Select all that apply.)** Why do we use population algorithms? 

(Select all that apply.) Why do we use population algorithms?

An algorithm that doesn't guarantee convergence but has proved to be useful to solve optimization problems

An algorithm that doesn't find the exact solution but we can control how good the approximation is

A category that comprises many different algorithms

What's a heuristic in the context of optimization?

What’s a heuristic in the context of optimization?

**(Select all that apply.)** Which options are valid genetic algorithm operations?  

(Select all that apply.) Which options are valid genetic algorithm operations?

The local best solution, which is the best solution found by that particle

The momentum, which is the current absolute value of the velocity of the particle

**(Select all that apply.)** Which factors are considered in modifying the velocity of a particle in PSO? 


(Select all that apply.) Which factors are considered in modifying the velocity of a particle in PSO?

Quiz on Population Algorithms

Quiz on Population Algorithms

Let's test our theoretical knowledge of population algorithms.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Quiz: Population Methods