Random Optimization: Fundamentals and Applications

Because it does not require the gradient of the problem to be optimized, random optimization (also known as RO) is a family of numerical optimization methods that can be used to functions that are neither continuous nor differentiable. These kinds of optimization approaches are also referred to as direct-search methods, derivative-free methods, and black-box methods.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Random optimization

Chapter 2: Mathematical optimization

Chapter 3: Gradient

Chapter 4: Continuous function

Chapter 5: Differentiable function

Chapter 6: Normal distribution

Chapter 7: Evolution strategy

Chapter 8: Unimodality

Chapter 9: Limit (mathematics)

Chapter 10: Probability distribution

(II) Answering the public top questions about random optimization.

(III) Real world examples for the usage of random optimization in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of random optimization' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of random optimization.