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.
Optimization theory seeks the best solution, which is pivotal for machine learning, cost-cutting in manufacturing, refining logi...Show More
WHAT YOU'LL LEARN
An understanding of the mathematical foundations of optimization methods
Familiarity with population-based metaheuristic optimization methods such as genetic algorithms and particle swarm optimization
Hands-on experience in formulating, implementing, and solving optimization problems using Python
A working knowledge of Python libraries such as SciPy, NumPy, and CVXPY for solving optimization problems
An understanding of the mathematical foundations of optimization methods
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Content
1.
Introduction
6 Lessons
Get familiar with optimization basics, Python tools, problem-solving, and practical applications.
2.
Derivatives and Gradients
8 Lessons
Walk through using derivatives and gradients for solving complex optimization problems in Python.
3.
First Optimization Algorithms
9 Lessons
Work your way through optimization algorithms including binary search, gradient descent, and Newton's method.
4.
Population Methods
11 Lessons
Enhance your skills in utilizing genetic algorithms and particle swarm optimization for complex problem-solving.
5.
Adding Constraints
6 Lessons
Take a closer look at solving constrained optimization problems using various methods and practical exercises in Python.
6.
Linear Constrained Optimization
7 Lessons
Tackle linear optimization problems using Python with SciPy and CVXPY, covering integer constraints and practical applications.
7.
Summary and Conclusion
2 Lessons
Master the steps to develop an optimization mindset and practical skills using Python.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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