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Development And Analysis Of An Optimal Bounding Ellipsoid Algorithm Using Stochastic Approximation
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Book Synopsis Development and Analysis of an Optimal Bounding Ellipsoid Algorithm Using Stochastic Approximation by : Ming-Shou Liu
Download or read book Development and Analysis of an Optimal Bounding Ellipsoid Algorithm Using Stochastic Approximation written by Ming-Shou Liu and published by . This book was released on 1993 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Bounding Ellipsoid Algorithms with Automatic Bound Estimation by : Tsung-Ming Lin
Download or read book Optimal Bounding Ellipsoid Algorithms with Automatic Bound Estimation written by Tsung-Ming Lin and published by . This book was released on 1996 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiweight Optimization in Optimal Bounding Ellipsoid Algorithms by : Dale Joachim
Download or read book Multiweight Optimization in Optimal Bounding Ellipsoid Algorithms written by Dale Joachim and published by . This book was released on 1998 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings written by and published by . This book was released on 1996 with total page 1042 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis American Doctoral Dissertations by :
Download or read book American Doctoral Dissertations written by and published by . This book was released on 1993 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1994 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimization for Machine Learning by : Suvrit Sra
Download or read book Optimization for Machine Learning written by Suvrit Sra and published by MIT Press. This book was released on 2012 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Book Synopsis Electrical & Electronics Abstracts by :
Download or read book Electrical & Electronics Abstracts written by and published by . This book was released on 1997 with total page 2304 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification of Parametric Models by : Eric Walter
Download or read book Identification of Parametric Models written by Eric Walter and published by . This book was released on 1997-01-14 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presentation of a coherent methodology for the estimation of the parameters of mathematical models from experimental data is examined in this volume. Many topics are covered including the choice of the structure of the mathematical model, the choice of a performance criterion to compare models, the optimization of this performance criterion, the evaluation of the uncertainty in the estimated parameters, the design of experiments so as to get the most relevant data and the critical analysis of results. There are also several features unique to the work such as an up-to-date presentation of the methodology for testing models for identifiability and distinguishability and a comprehensive treatment of parametric optimization which includes greater consider ation of numerical aspects and which examines recursive and non-recursive methods for linear and nonlinear models.
Book Synopsis Dynamics and Control of Trajectory Tubes by : Alexander B. Kurzhanski
Download or read book Dynamics and Control of Trajectory Tubes written by Alexander B. Kurzhanski and published by Springer. This book was released on 2014-10-27 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents theoretical methods involving the Hamilton–Jacobi–Bellman formalism in conjunction with set-valued techniques of nonlinear analysis to solve significant problems in dynamics and control. The emphasis is on issues of reachability, feedback control synthesis under complex state constraints, hard or double bounds on controls, and performance in finite time. Guaranteed state estimation, output feedback control, and hybrid dynamics are also discussed. Although the focus is on systems with linear structure, the authors indicate how to apply each approach to nonlinear and nonconvex systems. The main theoretical results lead to computational schemes based on extensions of ellipsoidal calculus that provide complete solutions to the problems. These computational schemes in turn yield software tools that can be applied effectively to high-dimensional systems. Ellipsoidal Techniques for Problems of Dynamics and Control: Theory and Computation will interest graduate and senior undergraduate students, as well as researchers and practitioners interested in control theory, its applications, and its computational realizations.
Book Synopsis Iterative Methods in Combinatorial Optimization by : Lap Chi Lau
Download or read book Iterative Methods in Combinatorial Optimization written by Lap Chi Lau and published by Cambridge University Press. This book was released on 2011-04-18 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of approximation algorithms for NP-hard combinatorial optimization problems, several techniques from exact optimization such as the primal-dual method have proven their staying power and versatility. This book describes a simple and powerful method that is iterative in essence and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids and flows. The presentation style is elementary enough to be accessible to anyone with exposure to basic linear algebra and graph theory, making the book suitable for introductory courses in combinatorial optimization at the upper undergraduate and beginning graduate levels. Discussions of advanced applications illustrate their potential for future application in research in approximation algorithms.
Book Synopsis Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems by : Sébastien Bubeck
Download or read book Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems written by Sébastien Bubeck and published by Now Pub. This book was released on 2012 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.
Book Synopsis Bandit Algorithms by : Tor Lattimore
Download or read book Bandit Algorithms written by Tor Lattimore and published by Cambridge University Press. This book was released on 2020-07-16 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
Book Synopsis Algorithms for Optimization by : Mykel J. Kochenderfer
Download or read book Algorithms for Optimization written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2019-03-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Book Synopsis Networked Multisensor Decision and Estimation Fusion by : Yunmin Zhu
Download or read book Networked Multisensor Decision and Estimation Fusion written by Yunmin Zhu and published by CRC Press. This book was released on 2012-07-05 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areas-including sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and i
Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Book Synopsis Foundations of Data Science by : Avrim Blum
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
