Random Optimization: Fundamentals and Applications
α’αααΈααααα βα’αα‘α·α ααααΌαα·αααα
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.
