Algorithms for Sparsity-Constrained Optimization

by Sohail Bahmani

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.

Product Details

ISBN-13:	9783319377193
Publisher:	Springer International Publishing
Publication date:	08/23/2016
Series:	Springer Theses , #261
Edition description:	Softcover reprint of the original 1st ed. 2014
Pages:	107
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

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

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.- Appendix A Proofs of Chapter 3.- Appendix B Proofs of Chapter 4.- Appendix C Proofs of Chapter 5.- Appendix D Proofs of Chapter 6.