000			03806cam a22004695i 4500
001			10512767
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005			20170603142559.0
006			m d
007			cr n
008			131015s2013 xxu| s |||| 0|eng d
020			_a9780817649470
024	7		_a10.1007/978-0-8176-4948-7 _2doi
035			_a(WaSeSS)ssj0000988209
040			_dWaSeSS
072		7	_aPDE _2bicssc
072		7	_aCOM014000 _2bisacsh
072		7	_aMAT003000 _2bisacsh
082	0	4	_a004.23 FOU-A
100	1		_aFoucart, Simon
245	1	2	_aMathematical introduction to compressive sensing _cby Simon Foucart, Holger Rauhut.
260			_aNew York : _bSpringer, _c©2013
300			_axviii, 625 p. : _bill. ; _c25 cm.
504			_aIncludes bibliographical references and index.
505	0		_a1 An Invitation to Compressive Sensing -- 2 Sparse Solutions of Underdetermined Systems -- 3 Basic Algorithms -- 4 Basis Pursuit -- 5 Coherence -- 6 Restricted Isometry Property -- 7 Basic Tools from Probability Theory -- 8 Advanced Tools from Probability Theory -- 9 Sparse Recovery with Random Matrices -- 10 Gelfand Widths of l1-Balls -- 11 Instance Optimality and Quotient Property -- 12 Random Sampling in Bounded Orthonormal Systems -- 13 Lossless Expanders in Compressive Sensing -- 14 Recovery of Random Signals using Deterministic Matrices -- 15 Algorithms for l1-Minimization -- Appendix A Matrix Analysis -- Appendix B Convex Analysis -- Appendix C Miscellanea -- List of Symbols -- References.
506			_aLicense restrictions may limit access.
520			_aAt the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. Key features include: · The first textbook completely devoted to the topic of compressive sensing · Comprehensive treatment of the subject, including background material from probability theory, detailed proofs of the main theorems, and an outline of possible applications · Numerous exercises designed to help students understand the material · An extensive bibliography with over 500 references that guide researchers through the literature With only moderate prerequisites, A Mathematical Introduction to Compressive Sensing is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject.
650		0	_aMathematics.
650		0	_aComputer science.
650		0	_aFunctional analysis.
650		0	_aTelecommunication.
650	1	4	_aMathematics.
650	2	4	_aComputational Science and Engineering.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aMath Applications in Computer Science.
650	2	4	_aCommunications Engineering, Networks.
650	2	4	_aFunctional Analysis.
700	1		_aRauhut, Holger
830		0	_aApplied and Numerical Harmonic Analysis,
856	4	0	_uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10512767 _zFull text available from SpringerLink ebooks - Mathematics and Statistics (2013)
942			_2ddc _cBK
999			_c10935 _d10935