Zhouchen Lin's Books

Alternating Direction Method of Multipliers for Machine Learning

By: Zhouchen Lin,Huan Li,Cong Fang

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Alternating Direction Method of Multipliers for Machine Learning

By: Zhouchen Lin,Huan Li,Cong Fang

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.

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Page Count

274

Publisher

Springer Nature

Published Date

ISBN 10

9811698406

ISBN 13

9789811698408

Low-Rank Models in Visual Analysis

Theories, Algorithms, and Applications

By: Zhouchen Lin,Hongyang Zhang

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Low-Rank Models in Visual Analysis

Theories, Algorithms, and Applications

By: Zhouchen Lin,Hongyang Zhang

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. - Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications - Provides a full and clear explanation of the theory behind the models - Includes detailed proofs in the appendices

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Page Count

262

Publisher

Academic Press

Published Date

ISBN 10

0128127325

ISBN 13

9780128127322

Accelerated Optimization for Machine Learning

First-Order Algorithms

By: Zhouchen Lin,Huan Li,Cong Fang

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Accelerated Optimization for Machine Learning

First-Order Algorithms

By: Zhouchen Lin,Huan Li,Cong Fang

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

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Page Count

286

Publisher

Springer Nature

Published Date

ISBN 10

9811529108

ISBN 13

9789811529108

Alternating Direction Method of Multipliers for Machine Learning

By: Zhouchen Lin,Huan Li,Cong Fang

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Alternating Direction Method of Multipliers for Machine Learning

By: Zhouchen Lin,Huan Li,Cong Fang

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

274

Publisher

Springer Nature

Published Date

ISBN 10

9811698406

ISBN 13

9789811698408

Book's by Zhouchen Lin

Alternating Direction Method of Multipliers for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning

Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis

Accelerated Optimization for Machine Learning

Accelerated Optimization for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning

Alternating Direction Method of Multipliers for Machine Learning

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