Sparse optimization theory and methods (Record no. 559959)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02403 a2200193 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190102172108.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190102b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781138080942 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIT Kanpur |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.6 |
| Item number | Z614s |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Zhao, Yun-Bin |
| 245 ## - TITLE STATEMENT | |
| Title | Sparse optimization theory and methods |
| Statement of responsibility, etc | Yun-Bin Zhao |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | CRC Press |
| Year of publication | 2018 |
| Place of publication | Boca Raton |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | ix, 284p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science.<br/><br/>Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author’s latest research in the field that have not appeared in other books. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Mathematical optimization |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| General Stacks | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 31/01/2019 | 60 | 9208.16 | 519.6 Z614s | A184278 | 11510.20 | Books |