borkar vivek (34 Ergebnisse)

Zustand: New.

Zustand: Good. 1st Edition. Ships from the UK. Used book that is in clean, average condition without any missing pages.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Zustand: New.

Hardcover. Zustand: New. Contents: 1. Introduction. 2. Convergence Analysis. 3. Finite time bounds and traps. 4. Stability Criteria. 5. Stochastic Recursive Inclusions. 6. Asynchronous Schemes. 7. A Limit Theorem for Fluctuations. 8. Multiple Timescales. 9. Constant Stepsize Algorithms. 10. General noise models. 11. Stochastic Gradient Schemes. 12. Liapunov and Related Systems. 13. Topics in Analysis. 14. Ordinary Differential Equations. 15. Topics in Probability. This book gives a comprehensive treatment of stochastic approximation algorithms using their differential equation limits, which lays bare its dynamical aspects. Highlights of the book include a streamlined treatment of classical results such as the analysis of asymptotic behavior for decreasing and constant stepsizes and the functional central limit theorem, and inclusion of several important extensions and recent developments such as concentration bounds, avoidance of traps, stability tests, asynchronous implementations, differential inclusion limits, multiple time scales, and general noise models. In addition, major applications are surveyed category-wise, with special focus on stochastic gradient descent. The book will be a valuable resource to students, researchers, and practitioners in statistics, applied probability, control and communication engineering, operations research, machine learning and economic models.

Hardcover. Zustand: New. This book builds up basic convex analysis and optimization together, using little beyond advanced calculus and very basic real analysis. Brief accounts of both are also included within the main text. The emphasis is on building a geometric intuition for the subject. This is aided further by figures. Two distinguishing features of the text are elementary alternative proofs of many results and an eclectic collection of useful facts from optimization and convexity often needed by researchers in optimization, game theory, control theory, mathematical economics, etc. In addition, there are pointers to more such results that are not covered here. A full chapter on optimization algorithms gives a bird's eye view of the field, touching upon many current themes. The book will be useful to students and researchers in the aforementioned fields, as also to the user community in various engineering disciplines.

Gebundene Ausgabe. Zustand: Sehr gut. 216 Seiten; Zustand geprüft, Versand werktags innerhalb von 24 Stunden. Sehr gut erhalten ZA 9638201 Sprache: Englisch Gewicht in Gramm: 494.

Paperback. Zustand: Very Good. Paperback. Superficial marks and light scores on covers. Spine ends, front leading corner and lower edges are very lightly bumped. Light bump on spine body. Light creases on leading edges and front spine side. Front leading corners are very lightly creased, slightly bending pages within. Light score on page block face. Pages are clean and tight throughout. T. Used.

Zustand: New. pp. ix + 164.

Paperback. Zustand: Brand New. 151 pages. 9.25x6.10x0.43 inches. In Stock.

Broschiert. Zustand: Sehr gut. Pitman Research Notes in Mathematics Series. Zust: Gutes Exemplar. 179 Seiten, Englisch 346g.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Taschenbuch. Zustand: Neu. Neuware -This book presents a selection of topics from probability theory. Essentially, the topics chosen are those that are likely to be the most useful to someone planning to pursue research in the modern theory of stochastic processes. The prospective reader is assumed to have good mathematical maturity. In particular, he should have prior exposure to basic probability theory at the level of, say, K.L. Chung's 'Elementary probability theory with stochastic processes' (Springer-Verlag, 1974) and real and functional analysis at the level of Royden's 'Real analysis' (Macmillan, 1968). The first chapter is a rapid overview of the basics. Each subsequent chapter deals with a separate topic in detail. There is clearly some selection involved and therefore many omissions, but that cannot be helped in a book of this size. The style is deliberately terse to enforce active learning. Thus several tidbits of deduction are left to the reader as labelled exercises in the main text of each chapter. In addition, there are supplementary exercises at the end. In the preface to his classic text on probability ('Probability', Addison Wesley, 1968), Leo Breiman speaks of the right and left hands of probability.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.

Hardcover. Zustand: Brand New. 1st edition. 164 pages. 9.25x6.00x0.50 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a selection of topics from probability theory. Essentially, the topics chosen are those that are likely to be the most useful to someone planning to pursue research in the modern theory of stochastic processes. The prospective reader is assumed to have good mathematical maturity. In particular, he should have prior exposure to basic probability theory at the level of, say, K.L. Chung's 'Elementary probability theory with stochastic processes' (Springer-Verlag, 1974) and real and functional analysis at the level of Royden's 'Real analysis' (Macmillan, 1968). The first chapter is a rapid overview of the basics. Each subsequent chapter deals with a separate topic in detail. There is clearly some selection involved and therefore many omissions, but that cannot be helped in a book of this size. The style is deliberately terse to enforce active learning. Thus several tidbits of deduction are left to the reader as labelled exercises in the main text of each chapter. In addition, there are supplementary exercises at the end. In the preface to his classic text on probability ('Probability', Addison Wesley, 1968), Leo Breiman speaks of the right and left hands of probability.

Taschenbuch. Zustand: Neu. Elementary Convexity with Optimization | Vivek S. Borkar (u. a.) | Taschenbuch | xii | Englisch | 2023 | Springer | EAN 9789819916511 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Neuware -This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch.

Taschenbuch. Zustand: Neu. Stochastic Approximation: A Dynamical Systems Viewpoint | Vivek S. Borkar | Taschenbuch | xv | Englisch | 2025 | Springer | EAN 9789819982790 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.

Paperback. Zustand: Brand New. 2012 edition. 202 pages. 9.25x6.10x0.51 inches. In Stock.

Hardcover. Zustand: Brand New. 2012 edition. 215 pages. 9.00x6.25x0.50 inches. In Stock.

Buch. Zustand: Neu. Neuware -This research monograph summarizes a line of research that mapscertain classical problems of discrete mathematics and operationsresearch - such as the Hamiltonian Cycle and the Travelling SalesmanProblems - into convex domains where continuum analysis can be carriedout. Arguably, the inherent difficulty of these, now classical,problems stems precisely from the discrete nature of domains in whichthese problems are posed. The convexification of domains underpinningthese results is achieved by assigning probabilistic interpretation tokey elements of the original deterministic problems. In particular,the approaches summarized here build on a technique that embedsHamiltonian Cycle and Travelling Salesman Problems in a structuredsingularly perturbed Markov decision process. The unifying idea is tointerpret subgraphs traced out by deterministic policies (includingHamiltonian cycles, if any) as extreme points of a convex polyhedron in a space filled with randomized policies.The above innovative approach has now evolved to the point where thereare many, both theoretical and algorithmic, results that exploit thenexus between graph theoretic structures and both probabilistic andalgebraic entities of related Markov chains. The latter includemoments of first return times, limiting frequencies of visits tonodes, or the spectra of certain matrices traditionally associatedwith the analysis of Markov chains. However, these results andalgorithms are dispersed over many research papers appearing injournals catering to disparate audiences. As a result, the publishedmanuscripts are often written in a very terse manner and use disparatenotation, thereby making it difficult for new researchers to make use of the many reported advances.Hence the main purpose of this book is to present a concise and yeteasily accessible synthesis of the majority of the theoretical andalgorithmicresults obtained so far. In addition, the book discussesnumerous open questions and problems that arise from this body of workand which are yet to be fully solved. The approach casts theHamiltonian Cycle Problem in a mathematical framework that permitsanalytical concepts and techniques, not used hitherto in this context,to be brought to bear to further clarify both the underlyingdifficulty of NP-completeness of this problem and the relativeexceptionality of truly difficult instances. Finally, the material isarranged in such a manner that the introductory chapters require verylittle mathematical background and discuss instances of graphs with interesting structures that motivated a lot of the research in this topic.More difficult results are introduced later and are illustrated withnumerous examples.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability and differential equations. Yet these algorithms have powerful applications in control and communications engineering, artificial intelligence and economic modelling. The dynamical systems viewpoint treats an algorithm as a noisy discretization of a limiting differential equation and argues that, under reasonable hypotheses, it tracks the asymptotic behaviour of the differential equation with probability one. The differential equation, which can usually be obtained by inspection, is easier to analyze. Novel topics include finite-time behaviour, multiple timescales and asynchronous implementation. There is a useful taxonomy of applications, with concrete examples from engineering and economics. Notably it covers variants of stochastic gradient-based optimization schemes, fixed-point solvers, which are commonplace in learning algorithms for approximate dynamic programming, and some models of collective behaviour. Three appendices give background on differential equations and probability.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research monograph summarizes a line of research that maps certain classical problems of discrete mathematics and operations research - such as the Hamiltonian Cycle and the Travelling Salesman Problems - into convex domains where continuum analysis can be carried out. Arguably, the inherent difficulty of these, now classical, problems stems precisely from the discrete nature of domains in which these problems are posed. The convexification of domains underpinning these results is achieved by assigning probabilistic interpretation to key elements of the original deterministic problems. In particular, the approaches summarized here build on a technique that embeds Hamiltonian Cycle and Travelling Salesman Problems in a structured singularly perturbed Markov decision process. The unifying idea is to interpret subgraphs traced out by deterministic policies (including Hamiltonian cycles, if any) as extreme points of a convex polyhedron in a space filled with randomized policies.The above innovative approach has now evolved to the point where there are many, both theoretical and algorithmic, results that exploit the nexus between graph theoretic structures and both probabilistic and algebraic entities of related Markov chains. The latter include moments of first return times, limiting frequencies of visits to nodes, or the spectra of certain matrices traditionally associated with the analysis of Markov chains. However, these results and algorithms are dispersed over many research papers appearing in journals catering to disparate audiences. As a result, the published manuscripts are often written in a very terse manner and use disparate notation, thereby making it difficult for new researchers to make use of the many reported advances.Hence the main purpose of this book is to present a concise and yet easily accessible synthesis of the majority of the theoretical and algorithmicresults obtained so far. In addition, the book discusses numerous open questions and problems that arise from this body of work and which are yet to be fully solved. The approach casts the Hamiltonian Cycle Problem in a mathematical framework that permits analytical concepts and techniques, not used hitherto in this context, to be brought to bear to further clarify both the underlying difficulty of NP-completeness of this problem and the relative exceptionality of truly difficult instances. Finally, the material is arranged in such a manner that the introductory chapters require very little mathematical background and discuss instances of graphs with interesting structures that motivated a lot of the research in this topic. More difficult results are introduced later and are illustrated with numerous examples.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research monograph summarizes a line of research that maps certain classical problems of discrete mathematics and operations research - such as the Hamiltonian Cycle and the Travelling Salesman Problems - into convex domains where continuum analysis can be carried out. Arguably, the inherent difficulty of these, now classical, problems stems precisely from the discrete nature of domains in which these problems are posed. The convexification of domains underpinning these results is achieved by assigning probabilistic interpretation to key elements of the original deterministic problems. In particular, the approaches summarized here build on a technique that embeds Hamiltonian Cycle and Travelling Salesman Problems in a structured singularly perturbed Markov decision process. The unifying idea is to interpret subgraphs traced out by deterministic policies (including Hamiltonian cycles, if any) as extreme points of a convex polyhedron in a space filled with randomized policies.The above innovative approach has now evolved to the point where there are many, both theoretical and algorithmic, results that exploit the nexus between graph theoretic structures and both probabilistic and algebraic entities of related Markov chains. The latter include moments of first return times, limiting frequencies of visits to nodes, or the spectra of certain matrices traditionally associated with the analysis of Markov chains. However, these results and algorithms are dispersed over many research papers appearing in journals catering to disparate audiences. As a result, the published manuscripts are often written in a very terse manner and use disparate notation, thereby making it difficult for new researchers to make use of the many reported advances.Hence the main purpose of this book is to present a concise and yet easily accessible synthesis of the majority of the theoretical and algorithmicresults obtained so far. In addition, the book discusses numerous open questions and problems that arise from this body of work and which are yet to be fully solved. The approach casts the Hamiltonian Cycle Problem in a mathematical framework that permits analytical concepts and techniques, not used hitherto in this context, to be brought to bear to further clarify both the underlying difficulty of NP-completeness of this problem and the relative exceptionality of truly difficult instances. Finally, the material is arranged in such a manner that the introductory chapters require very little mathematical background and discuss instances of graphs with interesting structures that motivated a lot of the research in this topic. More difficult results are introduced later and are illustrated with numerous examples.

Buch. Zustand: Neu. Neuware -This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch.