Bibliographic Notes¶

Following is a partial list of books and articles which have been referenced heavily in this work. This list is by no means exhaustive.

General introduction to optimization can be found in [29].
Main references for convex analysis are [7, 31].
[10] is a standard textbook for convex optimization theory, applications and algorithms.
[27] is a good reference for linear programming.
[9] covers alternating direction method of multipliers (ADMM) algorithms.
[30] provides good coverage on proximal algorithms.

Bibliography¶

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3: M. Artin. Algebra. Pearson Modern Classics for Advanced Mathematics Series. Pearson, 2017. ISBN 9780134689609. URL: https://books.google.co.in/books?id=ZfIXMQAACAAJ.
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5: Amir Beck. Introduction to nonlinear optimization: Theory, algorithms, and applications with MATLAB. SIAM, 2014.
6: Amir Beck. First-order methods in optimization. SIAM, 2017.
7: Dimitri Bertsekas, Angelia Nedic, and Asuman Ozdaglar. Convex analysis and optimization. Volume 1. Athena Scientific, 2003.
8: Stephen Boyd and Almir Mutapcic. Subgradient methods. 2008.
9: Stephen Boyd, Neal Parikh, and Eric Chu. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc, 2011.
10: Stephen P Boyd and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
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27: David G Luenberger, Yinyu Ye, and others. Linear and nonlinear programming. Volume 2. Springer, 1984.
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31: Ralph Tyrell Rockafellar. Convex analysis. Princeton university press, 2015.
32: Ron Rubinstein, Alfred M Bruckstein, and Michael Elad. Dictionaries for sparse representation modeling. Proceedings of the IEEE, 98(6):1045–1057, 2010.
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34: W.F. Trench. Introduction to Real Analysis. Open Textbook Library. Prentice Hall/Pearson Education, 2003. ISBN 9780130457868. URL: https://books.google.co.in/books?id=NkFkQgAACAAJ.
35: Joel A Tropp. Greed is good: algorithmic results for sparse approximation. Information Theory, IEEE Transactions on, 50(10):2231–2242, 2004.
36: Joel A Tropp. Just relax: convex programming methods for subset selection and sparse approximation. ICES report, 2004.
37: Joel A Tropp. Just relax: convex programming methods for identifying sparse signals in noise. Information Theory, IEEE Transactions on, 52(3):1030–1051, 2006.
38: Joel A Tropp and Anna C Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. Information Theory, IEEE Transactions on, 53(12):4655–4666, 2007.
39: Wikipedia contributors. Ordered pair — Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/Ordered_pair.
40: Wikipedia contributors. Relation (mathematics) — Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/Relation_(mathematics).
41: Wikipedia contributors. Tuple — Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/Tuple.

Topics in Signal Processing

Bibliographic Notes

Contents

Bibliographic Notes¶

Bibliography¶