000			04102nam a22005055i 4500
001			978-0-8176-4948-7
003			DE-He213
005			20251006084439.0
007			cr nn 008mamaa
008			130808s2013 xxu| s |||| 0|eng d
020			_a9780817649487
020			_a99780817649487
024	7		_a10.1007/978-0-8176-4948-7 _2doi
082	0	4	_a004 _223
100	1		_aFoucart, Simon. _eauthor.
245	1	2	_aA Mathematical Introduction to Compressive Sensing _h[electronic resource] / _cby Simon Foucart, Holger Rauhut.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Birkhäuser, _c2013.
300			_aXVIII, 625 p. 13 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aApplied and Numerical Harmonic Analysis, _x2296-5009
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.
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. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780817649470
830		0	_aApplied and Numerical Harmonic Analysis, _x2296-5009
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-8176-4948-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59830 _d59830