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

Yes, because they create a valid region that's hard to explore.

Not always. Some constraints make the problem easier to solve.

No, reducing the solution space simplifies the problem.

No, constraints don't affect the difficulty of the problem.

Are constrained problems more difficult?

Which is a method to remove constraints from a problem?

Which is a method to remove constraints from a problem?

A solution is the optimum among all the valid solutions.

A solution is the optimum value of the target function.

A solution should fulfill at least one constraint.

A solution for a constrained problem never exists.

What are the properties that must hold for a solution to a constrained problem?

What are the properties that must hold for a solution to a constrained problem?

Quiz on Constrained Optimization

Quiz on Constrained Optimization

Practice what we've learned about constrained optimization.

Introduction

Derivatives and Gradients

First Optimization Algorithms

Population Methods

Adding Constraints

Linear Constrained Optimization

Summary and Conclusion

Appendix

Quiz: Constraints