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Large Scale Convex Optimization
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Book Synopsis Large-Scale and Distributed Optimization by : Pontus Giselsson
Download or read book Large-Scale and Distributed Optimization written by Pontus Giselsson and published by Springer. This book was released on 2018-11-11 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.
Book Synopsis Large-Scale Convex Optimization by : Ernest K. Ryu
Download or read book Large-Scale Convex Optimization written by Ernest K. Ryu and published by Cambridge University Press. This book was released on 2022-11-30 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified analysis of first-order optimization methods, including parallel-distributed algorithms, using monotone operators.
Book Synopsis Methods for Large-scale Convex Optimization Problems with L1 Regularization by : Kwangmoo Koh
Download or read book Methods for Large-scale Convex Optimization Problems with L1 Regularization written by Kwangmoo Koh and published by . This book was released on 2008 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Much of recent research in signal processing, statistics, and many other fields has focused on ℓ1 regularization based methods for feature selection, sparse signal reconstruction. In this thesis we study optimization problems with ℓ1 regularization, and efficient methods to solve them.
Book Synopsis Large-Scale Convex Optimization by : Ernest K. Ryu
Download or read book Large-Scale Convex Optimization written by Ernest K. Ryu and published by Cambridge University Press. This book was released on 2022-12-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms.
Book Synopsis Online Optimization of Large Scale Systems by : Martin Grötschel
Download or read book Online Optimization of Large Scale Systems written by Martin Grötschel and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: In its thousands of years of history, mathematics has made an extraordinary ca reer. It started from rules for bookkeeping and computation of areas to become the language of science. Its potential for decision support was fully recognized in the twentieth century only, vitally aided by the evolution of computing and communi cation technology. Mathematical optimization, in particular, has developed into a powerful machinery to help planners. Whether costs are to be reduced, profits to be maximized, or scarce resources to be used wisely, optimization methods are available to guide decision making. Opti mization is particularly strong if precise models of real phenomena and data of high quality are at hand - often yielding reliable automated control and decision proce dures. But what, if the models are soft and not all data are around? Can mathematics help as well? This book addresses such issues, e. g. , problems of the following type: - An elevator cannot know all transportation requests in advance. In which order should it serve the passengers? - Wing profiles of aircrafts influence the fuel consumption. Is it possible to con tinuously adapt the shape of a wing during the flight under rapidly changing conditions? - Robots are designed to accomplish specific tasks as efficiently as possible. But what if a robot navigates in an unknown environment? - Energy demand changes quickly and is not easily predictable over time. Some types of power plants can only react slowly.
Book Synopsis Convex Optimization Algorithms by : Dimitri Bertsekas
Download or read book Convex Optimization Algorithms written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2015-02-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and accessible presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. This is facilitated by the extensive use of analytical and algorithmic concepts of duality, which by nature lend themselves to geometrical interpretation. The book places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning. The book is aimed at students, researchers, and practitioners, roughly at the first year graduate level. It is similar in style to the author's 2009"Convex Optimization Theory" book, but can be read independently. The latter book focuses on convexity theory and optimization duality, while the present book focuses on algorithmic issues. The two books share notation, and together cover the entire finite-dimensional convex optimization methodology. To facilitate readability, the statements of definitions and results of the "theory book" are reproduced without proofs in Appendix B.
Book Synopsis Large-scale Optimization by : Vladimir Tsurkov
Download or read book Large-scale Optimization written by Vladimir Tsurkov and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms. Audience: This book is addressed to specialists in operations research, optimization, and optimal control.
Book Synopsis Convex Optimization Theory by : Dimitri Bertsekas
Download or read book Convex Optimization Theory written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2009-06-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: An insightful, concise, and rigorous treatment of the basic theory of convex sets and functions in finite dimensions, and the analytical/geometrical foundations of convex optimization and duality theory. Convexity theory is first developed in a simple accessible manner, using easily visualized proofs. Then the focus shifts to a transparent geometrical line of analysis to develop the fundamental duality between descriptions of convex functions in terms of points, and in terms of hyperplanes. Finally, convexity theory and abstract duality are applied to problems of constrained optimization, Fenchel and conic duality, and game theory to develop the sharpest possible duality results within a highly visual geometric framework. This on-line version of the book, includes an extensive set of theoretical problems with detailed high-quality solutions, which significantly extend the range and value of the book. The book may be used as a text for a theoretical convex optimization course; the author has taught several variants of such a course at MIT and elsewhere over the last ten years. It may also be used as a supplementary source for nonlinear programming classes, and as a theoretical foundation for classes focused on convex optimization models (rather than theory). It is an excellent supplement to several of our books: Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 2017), Network Optimization(Athena Scientific, 1998), Introduction to Linear Optimization (Athena Scientific, 1997), and Network Flows and Monotropic Optimization (Athena Scientific, 1998).
Book Synopsis Algorithms for Convex Optimization by : Nisheeth K. Vishnoi
Download or read book Algorithms for Convex Optimization written by Nisheeth K. Vishnoi and published by Cambridge University Press. This book was released on 2021-10-07 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.
Book Synopsis Large-Scale Nonlinear Optimization by : Gianni Pillo
Download or read book Large-Scale Nonlinear Optimization written by Gianni Pillo and published by Springer Science & Business Media. This book was released on 2006-06-03 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.
Book Synopsis A Computional Study of the Homogenous Algorithm for Large-scale Convex Optimization by :
Download or read book A Computional Study of the Homogenous Algorithm for Large-scale Convex Optimization written by and published by . This book was released on 1996 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Convex Optimization by : Stephen P. Boyd
Download or read book Convex Optimization written by Stephen P. Boyd and published by Cambridge University Press. This book was released on 2004-03-08 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Book Synopsis Tools and Methods for Large-scale Convex Optimization by : Eric Chu
Download or read book Tools and Methods for Large-scale Convex Optimization written by Eric Chu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Convex optimization is widely used in many areas of engineering science such as control theory, statistics and machine learning, and image and signal processing. There are, however, several barriers to the use of convex optimization in everyday engineering: solvers for convex optimization in general require specialized knowledge to code, and in order to solve problems, users must typically perform tedious manual transformations before calling a solver. This problem is exacerbated when problem sizes become extremely large. In this thesis, we investigate tools and methods to address these two issues in the context of large-scale convex optimization. In particular, we develop technology to handle very large problems, including a large-scale solver and a tool to model potentially large optimization problems. This tool allows users to describe their problems with an intuitive model that is automatically transformed into a form handled by the large-scale solver, liberating users from performing tedious manual transformations. For the large-scale solver, we use the alternating direction method of multipliers (ADMM) and express conic optimization problems in consensus form, splitting the linear algebra from the generalized conic inequalities. For modeling optimization problems, we present the quadratic cone modeling language (QCML), which like CVX is a tool that automatically converts convex optimization problems into conic form and solves them with a standard cone solver. Unlike CVX, QCML can be used to analyze and generate code for entire problem families without requiring another analysis or generation phase when problem (instance) data or dimensions change.
Book Synopsis Lectures on Modern Convex Optimization by : Aharon Ben-Tal
Download or read book Lectures on Modern Convex Optimization written by Aharon Ben-Tal and published by SIAM. This book was released on 2001-01-01 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming. The authors present the basic theory underlying these problems as well as their numerous applications in engineering, including synthesis of filters, Lyapunov stability analysis, and structural design. The authors also discuss the complexity issues and provide an overview of the basic theory of state-of-the-art polynomial time interior point methods for linear, conic quadratic, and semidefinite programming. The book's focus on well-structured convex problems in conic form allows for unified theoretical and algorithmical treatment of a wide spectrum of important optimization problems arising in applications.
Book Synopsis Large-scale Numerical Optimization by : Thomas Frederick Coleman
Download or read book Large-scale Numerical Optimization written by Thomas Frederick Coleman and published by SIAM. This book was released on 1990-01-01 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers from a workshop held at Cornell University, Oct. 1989, and sponsored by Cornell's Mathematical Sciences Institute. Annotation copyright Book News, Inc. Portland, Or.
Author :Prateek Jain Publisher :Foundations and Trends in Machine Learning ISBN 13 :9781680833683 Total Pages :218 pages Book Rating :4.8/5 (336 download)
Book Synopsis Non-convex Optimization for Machine Learning by : Prateek Jain
Download or read book Non-convex Optimization for Machine Learning written by Prateek Jain and published by Foundations and Trends in Machine Learning. This book was released on 2017-12-04 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Novel Gradient-type Optimization Algorithms for Extremely Large-scale Nonsmooth Convex Optimization by : Elena Olvovsky
Download or read book Novel Gradient-type Optimization Algorithms for Extremely Large-scale Nonsmooth Convex Optimization written by Elena Olvovsky and published by . This book was released on 2006 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:
