Download PDF by Sohail Bahmani: Algorithms for Sparsity-Constrained Optimization

By Sohail Bahmani

ISBN-10: 3319018809

ISBN-13: 9783319018805

ISBN-10: 3319018817

ISBN-13: 9783319018812

This thesis demonstrates recommendations that offer quicker and extra actual options to numerous difficulties in desktop studying and sign processing. the writer proposes a "greedy" set of rules, deriving sparse ideas with promises of optimality. using this set of rules gets rid of a number of the inaccuracies that happened with using past models.

Show description

Read Online or Download Algorithms for Sparsity-Constrained Optimization PDF

Similar algorithms books

Get How to Solve It: Modern Heuristics (2nd Edition) PDF

Uploader's observe: Ripped from SpringerLink.

Amazon hyperlink: http://www. amazon. com/How-Solve-It-Modern-Heuristics/dp/3540224947

This ebook is the one resource that gives finished, present, and proper details on challenge fixing utilizing smooth heuristics. It covers vintage tools of optimization, together with dynamic programming, the simplex procedure, and gradient ideas, in addition to fresh recommendations resembling simulated annealing, tabu seek, and evolutionary computation. built-in into the discourse is a chain of difficulties and puzzles to problem the reader. The booklet is written in a full of life, attractive sort and is meant for college students and practitioners alike. someone who reads and is aware the cloth within the e-book may be armed with the main strong challenge fixing instruments presently known.

This moment variation includes new chapters, one on coevolutionary structures and one on multicriterial decision-making. additionally a few new puzzles are extra and diverse subchapters are revised.

New PDF release: Geometric approximation algorithms

Specific algorithms for facing geometric items are complex, tough to enforce in perform, and sluggish. over the past two decades a thought of geometric approximation algorithms has emerged. those algorithms are typically easy, quickly, and extra powerful than their specific opposite numbers. This e-book is the 1st to hide geometric approximation algorithms intimately.

Download e-book for kindle: Dynamic Reconfiguration Architectures and Algorithms by Ramachandran Vaidyanathan

Dynamic Reconfiguration: Architectures and Algorithms deals a complete remedy of dynamically reconfigurable machine architectures and algorithms for them. The assurance is vast ranging from primary algorithmic suggestions, ranging throughout algorithms for a big selection of difficulties and functions, to simulations among versions.

Extra resources for Algorithms for Sparsity-Constrained Optimization

Sample text

S. Shalev-Shwartz, N. Srebro, and T. Zhang. Trading accuracy for sparsity in optimization problems with sparsity constraints. SIAM Journal on Optimization, 20(6):2807–2832, 2010. A. Tewari, P. K. Ravikumar, and I. S. Dhillon. Greedy algorithms for structurally constrained high dimensional problems. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 24, pages 882–890. 2011. J. A. Tropp. User-friendly tail bounds for sums of random matrices.

2009) where regularization with “decomposable” norms is considered in M-estimation problems. To provide the accuracy guarantees, these works generalize the Restricted Eigenvalue condition Bickel et al. (2009) to ensure that the loss function is strongly convex over a restriction of its domain. We would like to emphasize that these sufficient conditions generally hold with proper constants and with high probability only if one assumes that the true parameter is bounded. , Bunea 2008; Kakade et al.

Dahmen, and R. DeVore. Compressed sensing and best k-term approximation. Journal of American Mathematical Society, 22(1):211–231, Jan. 2009. 10 2 Preliminaries W. Dai and O. Milenkovic. Subspace pursuit for compressive sensing signal reconstruction. IEEE Transactions on Information Theory, 55(5):2230–2249, 2009. A. J. Dobson and A. Barnett. An Introduction to Generalized Linear Models. Chapman and Hall/CRC, Boca Raton, FL, 3rd edition, May 2008. ISBN 9781584889502. D. L. Donoho. Compressed sensing.

Download PDF sample

Algorithms for Sparsity-Constrained Optimization by Sohail Bahmani

by Kenneth

Rated 4.36 of 5 – based on 39 votes