Practical Mathematical Optimization: Basic Optimization Theory and Gradient-Based Algorithms (Springer Optimization and Its Applications)

Snyman, Jan A, Wilke, Daniel N

ISBN 10: 3319775855 / ISBN 13: 9783319775852

Editore: Springer, 2018

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Codice articolo ERICA77533197758556

Dati bibliografici

• Titolo: Practical Mathematical Optimization: Basic Optimization Theory and Gradient-Based Algorithms (Springer Optimization and Its Applications)
• Editore: Springer
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319775855
• ISBN 13: 9783319775852
• Rilegatura: Hardcover
• Edizione: seconda edizione
• Condizione: New
• Tipo di libro: book

Descrizione articolo

Riassunto

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

Informazioni sull?autore

Jan A. Snyman currently holds the position of emeritus professor in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria, having retired as full professor in 2005. He has taught physics, mathematics and engineering mechanics to science and engineering students at undergraduate and postgraduate level, and has supervised the theses of 26 Masters and 8 PhD students. His research mainly concerns the development of gradient-based trajectory optimization algorithms for solving noisy and multi-modal problems, and their application in approximation methodologies for the optimal design of engineering systems. He has authored or co-authored 89 research articles in peer-reviewed journals as well as numerous papers in international conference proceedings.

Daniel N. Wilke is a senior lecturer in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria.   He teaches computer programming, mathematical programming and computational mechanics to science and engineering students at undergraduate and postgraduate level. His current research focuses on the development of interactive design optimization technologies, and enabling statistical learning (artificial intelligence) application layers, for industrial processes and engineering design. He has co-authored over 50 peer-reviewed journal articles and full length conference papers.