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Algorithms for sparsity-constrained optimization [electronic resource] / Sohail Bahmani.

Bahmani, Sohail, author.
2014
QA402.5
Available Online
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Title
Algorithms for sparsity-constrained optimization [electronic resource] / Sohail Bahmani.
Author
Bahmani, Sohail, author.
ISBN
9783319018812 electronic book
3319018817 electronic book
9783319018805
DOI
https://doi.org/10.1007/978-3-319-01881-2
Published
Cham : Springer, 2014.
Language
English
Description
1 online resource (xxi, 107 pages) : illustrations (some color).
Item Number
10.1007/978-3-319-01881-2 doi
Call Number
QA402.5
Dewey Decimal Classification
519.6
Summary
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.
Dissertation Note
Ph.D. Carnegie Mellon University [2013?]
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed October 14, 2013).
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Springer theses.
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Introduction
Preliminaries
Sparsity-Constrained Optimization
Background
1-bit Compressed Sensing
Estimation Under Model-Based Sparsity
Projected Gradient Descent for lp-constrained Least Squares
Conclusion and Future Work.

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