Press "Enter" to skip to content

Sohail Bahmani's Algorithms for Sparsity-Constrained Optimization PDF

By Sohail Bahmani

This thesis demonstrates recommendations that supply swifter and extra exact strategies to quite a few difficulties in desktop studying and sign processing. the writer proposes a "greedy" set of rules, deriving sparse options 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 or Download Algorithms for Sparsity-Constrained Optimization PDF

Similar algorithms books

Algorithmic and Analysis Techniques in Property Testing - download pdf or read online

Estate trying out algorithms convey a desirable connection among worldwide houses of gadgets and small, neighborhood perspectives. Such algorithms are "ultra"-efficient to the level that they simply learn a tiny part of their enter, and but they come to a decision even if a given item has a definite estate or is considerably assorted from any item that has the valuables.

Get Graph Data Model: and Its Data Language PDF

Advanced databases will be understood good with visible illustration. A graph is a really intuitive and rational constitution to visually symbolize such databases. Graph information version (GDM) proposed through the writer formalizes facts illustration and operations at the facts by way of the graph thought. The GDM is an extension of the relational version towards structural illustration.

Digital Fourier Analysis: Fundamentals by Ken'iti Kido PDF

This textbook is a radical, obtainable creation to electronic Fourier research for undergraduate scholars within the sciences. starting with the foundations of sine/cosine decomposition, the reader walks during the rules of discrete Fourier research earlier than attaining the cornerstone of sign processing: the quick Fourier remodel.

Additional info for Algorithms for Sparsity-Constrained Optimization

Example text

Shechtman, Y. C. Eldar, A. Szameit, and M. Segev. Sparsity based sub-wavelength imaging with partially incoherent light via quadratic compressed sensing. Optics Express, 19(16):14807–14822, July 2011a. Y. Shechtman, A. Szameit, E. Osherovic, E. Bullkich, H. Dana, S. Gazit, S. Shoham, M. Zibulevsky, I. Yavneh, E. B. Kley, Y. C. Eldar, O. Cohen, and M. Segev. Sparsity-based single-shot sub-wavelength coherent diffractive imaging. In Frontiers in Optics, OSA Technical Digest, page PDPA3. Optical Society of America, Oct.

References A. Agarwal, S. Negahban, and M. Wainwright. Fast global convergence rates of gradient methods for high-dimensional statistical recovery. In J. Lafferty, C. K. I. Williams, J. ShaweTaylor, R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems, volume 23, pages 37–45. 2010. ML]. A. Beck and Y. C. Eldar. Sparsity constrained nonlinear optimization: Optimality conditions and algorithms. IT], Mar. 2012. P. Bickel, Y. Ritov, and A. Tsybakov. Simultaneous analysis of Lasso and Dantzig selector.

The results of both GraSP methods with “debiasing” are also included. The average loss at the true parameter and one standard p deviation interval around it are plotted as well. 5 Simulations 29 c ρ = 1/2 d ρ= √ 2/2 Fig. 1 (continued) on the plots. Furthermore, we evaluated performance of GraSP with the debiasing procedure described in Sect. 1. As can be seen from the figure at lower values of the sampling ratio GraSP is not accurate and does not seem to be converging. This behavior can be explained by the fact that without regularization at low sampling ratios the training data is 30 3 Sparsity-Constrained Optimization linearly separable or has very few mislabelled samples.

Download PDF sample

Rated 4.10 of 5 – based on 24 votes