Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers PDF full book. Access full book title Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers by Stephen Boyd. Download full books in PDF and EPUB format.
Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138
Get Book
Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138
Get Book
Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author: Zhouchen Lin
Publisher: Springer Nature
ISBN: 9811698406
Category : Computers
Languages : en
Pages : 274
Get Book
Book Description
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
Author: William Fitzgibbon
Publisher: Springer
ISBN: 940179054X
Category : Technology & Engineering
Languages : en
Pages : 248
Get Book
Book Description
This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first conference, “Optimization and PDEs with Industrial Applications” celebrated the seventieth birthday of Professor Jacques Périaux of the University of Jyväskylä and Polytechnic University of Catalonia (Barcelona Tech) and the second conference, “Optimization and PDEs with Applications” celebrated the seventy-fifth birthday of Professor Roland Glowinski of the University of Houston. This work should be of interest to researchers and practitioners as well as advanced students or engineers in computational and applied mathematics or mechanics.
Author: Neal Parikh
Publisher: Now Pub
ISBN: 9781601987167
Category : Mathematics
Languages : en
Pages : 130
Get Book
Book Description
Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods. Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice.
Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
ISBN: 1786305445
Category : Business & Economics
Languages : en
Pages : 460
Get Book
Book Description
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Author: Guanghui Lan
Publisher: Springer Nature
ISBN: 3030395685
Category : Mathematics
Languages : en
Pages : 591
Get Book
Book Description
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Author: Stephen P. Boyd
Publisher: Cambridge University Press
ISBN: 9780521833783
Category : Business & Economics
Languages : en
Pages : 744
Get Book
Book Description
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.
Author: Jeremy Watt
Publisher: Cambridge University Press
ISBN: 1108480721
Category : Computers
Languages : en
Pages : 597
Get Book
Book Description
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author: Roland Glowinski
Publisher: SIAM
ISBN: 0898712300
Category : Science
Languages : en
Pages : 301
Get Book
Book Description
This volume deals with the numerical simulation of the behavior of continuous media by augmented Lagrangian and operator-splitting methods.
Author: Roland Glowinski
Publisher: Springer
ISBN: 3319415891
Category : Mathematics
Languages : en
Pages : 820
Get Book
Book Description
This book is about computational methods based on operator splitting. It consists of twenty-three chapters written by recognized splitting method contributors and practitioners, and covers a vast spectrum of topics and application areas, including computational mechanics, computational physics, image processing, wireless communication, nonlinear optics, and finance. Therefore, the book presents very versatile aspects of splitting methods and their applications, motivating the cross-fertilization of ideas.
