04096cam 22004815i 450000100090000000300060000900500170001500600190003200700150005100800410006602000180010702400350012503500260016004000110018607200160019707200230021307200230023608200170025910000190027624500870029526000340038230000360041650400510045250507100050350600430121352015460125665000170280265000220281965000250284165000230286665000170288965000430290665000410294965000430299065000420303365000250307570000190310083000450311985601430316494200120330799900170331995202780333610512767IIITD20170603142559.0m d cr n 131015s2013 xxu| s |||| 0|eng d a97808176494707 a10.1007/978-0-8176-4948-72doi a(WaSeSS)ssj0000988209 dWaSeSS 7aPDE2bicssc 7aCOM0140002bisacsh 7aMAT0030002bisacsh04a004.23 FOU-A1 aFoucart, Simon12aMathematical introduction to compressive sensingcby Simon Foucart, Holger Rauhut. aNew York :bSpringer,cÃ2013 axviii, 625 p. :bill. ;c25 cm. aIncludes bibliographical references and index.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. aLicense restrictions may limit access. 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. 0aMathematics. 0aComputer science. 0aFunctional analysis. 0aTelecommunication.14aMathematics.24aComputational Science and Engineering.24aSignal, Image and Speech Processing.24aMath Applications in Computer Science.24aCommunications Engineering, Networks.24aFunctional Analysis.1 aRauhut, Holger 0aApplied and Numerical Harmonic Analysis,40uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio10512767zFull text available from SpringerLink ebooks - Mathematics and Statistics (2013) 2ddccBK c10935d10935 00102ddc406REF_004_230000000000000_FOUA718CSE915205aIIITbIIITcCARTd2015-08-13g2330l2m2oREF 004.23 FOU-Ap005212r2016-09-14s2016-07-25vÈ 42.99w2015-08-13yBKeIN11588/15-16f2015-08-01jIIITD/LIC/BS/2012/02/137k2015-06-04xShankar's Book Agency Pvt. Ltd.