Introduction to Global Optimization Exploiting Space-Filling Curves provides an overview of classical and new results pertaining to the usage of space-filling curves in global optimization. The authors look at a family of derivative-free numerical algorithms applying space-filling curves to reduce the dimensionality of the global optimization problem; along with a number of unconventional ideas, such as adaptive strategies for estimating Lipschitz constant, balancing global and local information to accelerate the search. Convergence conditions of the described algorithms are studied in depth and theoretical considerations are illustrated through numerical examples. This work also contains a code for implementing space-filling curves that can be used for constructing new global optimization algorithms. Basic ideas from this text can be applied to a number of problems including problems with multiextremal and partially defined constraints and non-redundant parallel computations can be organized. Professors, students, researchers, engineers, and other professionals in the fields of pure mathematics, nonlinear sciences studying fractals, operations research, management science, industrial and applied mathematics, computer science, engineering, economics, and the environmental sciences will find this title useful .
Maybe someone in your family bought you this book to read, and you thought, “Yeah, sure, why not? They’re short stories, so it shouldn’t take me too long to read; and there isn’t really continuity, so I don’t have to worry about putting it down for a while if I get busy.” You were maybe a little worried because the book did seem sort of long, but you were into it. You turned it over to read the back, and this was what you encounter. Some second-person narration that seemed like a complete rip-off of Italo Calvino’s If on a Winter’s Night a Traveler—but, you liked that book. What’s this book about (“Yes, finally,” you say)? It’s about different things. One story is about a pop culture–loving android named Biff, while yet another involves a genderless narrator and a baseball bat. If you’re looking for themes, some keywords are mortality, loss, pop culture, originality, cynicism, fantasy, and of course, work. Does that draw you in enough to crack open the book now, or will you wait and, in true Calvino fashion, get comfortable before reading? If you’ve read this far, you’ll probably like this book. But maybe you think it’s obtuse. Read the book and find out.
Introduction to Global Optimization Exploiting Space-Filling Curves provides an overview of classical and new results pertaining to the usage of space-filling curves in global optimization. The authors look at a family of derivative-free numerical algorithms applying space-filling curves to reduce the dimensionality of the global optimization problem; along with a number of unconventional ideas, such as adaptive strategies for estimating Lipschitz constant, balancing global and local information to accelerate the search. Convergence conditions of the described algorithms are studied in depth and theoretical considerations are illustrated through numerical examples. This work also contains a code for implementing space-filling curves that can be used for constructing new global optimization algorithms. Basic ideas from this text can be applied to a number of problems including problems with multiextremal and partially defined constraints and non-redundant parallel computations can be organized. Professors, students, researchers, engineers, and other professionals in the fields of pure mathematics, nonlinear sciences studying fractals, operations research, management science, industrial and applied mathematics, computer science, engineering, economics, and the environmental sciences will find this title useful .
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