Non-Convex Multi-Objective Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Non-Convex Multi-Objective Optimization write by Panos M. Pardalos. This book was released on 2017-07-27. Non-Convex Multi-Objective Optimization available in PDF, EPUB and Kindle. Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.
Non-convex Optimization for Machine Learning
Non-convex Optimization for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Non-convex Optimization for Machine Learning write by Prateek Jain. This book was released on 2017-12-04. Non-convex Optimization for Machine Learning available in PDF, EPUB and Kindle. Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.
Optimization on Low Rank Nonconvex Structures
Optimization on Low Rank Nonconvex Structures - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Optimization on Low Rank Nonconvex Structures write by Hiroshi Konno. This book was released on 2013-12-01. Optimization on Low Rank Nonconvex Structures available in PDF, EPUB and Kindle. Global optimization is one of the fastest developing fields in mathematical optimization. In fact, an increasing number of remarkably efficient deterministic algorithms have been proposed in the last ten years for solving several classes of large scale specially structured problems encountered in such areas as chemical engineering, financial engineering, location and network optimization, production and inventory control, engineering design, computational geometry, and multi-objective and multi-level optimization. These new developments motivated the authors to write a new book devoted to global optimization problems with special structures. Most of these problems, though highly nonconvex, can be characterized by the property that they reduce to convex minimization problems when some of the variables are fixed. A number of recently developed algorithms have been proved surprisingly efficient for handling typical classes of problems exhibiting such structures, namely low rank nonconvex structures. Audience: The book will serve as a fundamental reference book for all those who are interested in mathematical optimization.
Non-convex and Multi-objective Optimization in Data Mining
Non-convex and Multi-objective Optimization in Data Mining - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Non-convex and Multi-objective Optimization in Data Mining write by Ingo Mierswa. This book was released on 2009. Non-convex and Multi-objective Optimization in Data Mining available in PDF, EPUB and Kindle.
Multi-Objective Optimization using Evolutionary Algorithms
Multi-Objective Optimization using Evolutionary Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multi-Objective Optimization using Evolutionary Algorithms write by Kalyanmoy Deb. This book was released on 2001-07-05. Multi-Objective Optimization using Evolutionary Algorithms available in PDF, EPUB and Kindle. Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.
