Algorithms for Sparsity-Constrained Optimization
Author:   Sohail Bahmani
Publisher:   Springer International Publishing AG
Edition:   2014 ed.
Volume:   261
ISBN:  

9783319018805


Pages:   107
Publication Date:   18 October 2013
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Algorithms for Sparsity-Constrained Optimization


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Overview

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a ""greedy"" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

Full Product Details

Author:   Sohail Bahmani
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2014 ed.
Volume:   261
Dimensions:   Width: 15.50cm , Height: 1.30cm , Length: 23.50cm
Weight:   3.497kg
ISBN:  

9783319018805


ISBN 10:   3319018809
Pages:   107
Publication Date:   18 October 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction.- Preliminaries.- Sparsity-Constrained Optimization.- Background.- 1-bit Compressed Sensing.- Estimation Under Model-Based Sparsity.- Projected Gradient Descent for `p-constrained Least Squares.- Conclusion and Future Work.

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Author Information

Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.

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