Bibliographic Notes
Contents
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 [18].
[8] is a standard textbook for convex optimization theory, applications and algorithms.
[17] is a good reference for linear programming.
[7] covers alternating direction method of multipliers (ADMM) algorithms.
[19] provides good coverage on proximal algorithms.
Bibliography#
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M. Artin. Algebra. Pearson Modern Classics for Advanced Mathematics Series. Pearson, 2017. ISBN 9780134689609. URL: https://books.google.co.in/books?id=ZfIXMQAACAAJ.
- 3
Amir Beck. Introduction to nonlinear optimization: Theory, algorithms, and applications with MATLAB. SIAM, 2014.
- 4
Amir Beck. First-order methods in optimization. SIAM, 2017.
- 5
Dimitri Bertsekas, Angelia Nedic, and Asuman Ozdaglar. Convex analysis and optimization. Volume 1. Athena Scientific, 2003.
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Stephen Boyd and Almir Mutapcic. Subgradient methods. 2008.
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Stephen Boyd, Neal Parikh, and Eric Chu. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc, 2011.
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Stephen P Boyd and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
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Emmanuel J Candes and Terence Tao. Decoding by linear programming. Information Theory, IEEE Transactions on, 51(12):4203–4215, 2005.
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Emmanuel J Candes and Terence Tao. Near-optimal signal recovery from random projections: universal encoding strategies? Information Theory, IEEE Transactions on, 52(12):5406–5425, 2006.
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David L Donoho and Michael Elad. Optimally sparse representation in general (nonorthogonal) dictionaries via $l_1$ minimization. Proceedings of the National Academy of Sciences, 100(5):2197–2202, 2003.
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Michael Elad. Sparse and redundant representations. Springer, 2010.
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Michael Elad and Alfred M Bruckstein. A generalized uncertainty principle and sparse representation in pairs of bases. Information Theory, IEEE Transactions on, 48(9):2558–2567, 2002.
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D. Gopal, A. Deshmukh, A.S. Ranadive, and S. Yadav. An Introduction to Metric Spaces. CRC Press, 2020. ISBN 9781000087994. URL: https://books.google.co.in/books?id=13jtDwAAQBAJ.
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Jean-Baptiste Hiriart-Urruty and Claude Lemaréchal. Convex analysis and minimization algorithms I: Fundamentals. Volume 305. Springer science & business media, 2013.
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Kenneth Hoffman and Ray Kunze. Linear algebra, prentice-hall. Inc., Englewood Cliffs, New Jersey, pages 122–125, 1971.
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David G Luenberger, Yinyu Ye, and others. Linear and nonlinear programming. Volume 2. Springer, 1984.
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Jorge Nocedal and Stephen Wright. Numerical optimization. Springer Science & Business Media, 2006.
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Neal Parikh and Stephen Boyd. Proximal algorithms. Foundations and Trends in optimization, 1(3):127–239, 2014.
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Ralph Tyrell Rockafellar. Convex analysis. Princeton university press, 2015.
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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.
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Joel A Tropp. Greed is good: algorithmic results for sparse approximation. Information Theory, IEEE Transactions on, 50(10):2231–2242, 2004.
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Joel A Tropp. Just relax: convex programming methods for subset selection and sparse approximation. ICES report, 2004.
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Joel A Tropp. Just relax: convex programming methods for identifying sparse signals in noise. Information Theory, IEEE Transactions on, 52(3):1030–1051, 2006.
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Wikipedia contributors. Relation (mathematics) — Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/Relation_(mathematics).
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Wikipedia contributors. Tuple — Wikipedia, the free encyclopedia. URL: https://en.wikipedia.org/wiki/Tuple.