LEADER 05410nam 2200733 a 450 001 9910452096603321 005 20200520144314.0 010 $a1-280-66966-7 010 $a9786613646590 010 $a981-4366-29-3 035 $a(CKB)2550000000101468 035 $a(EBL)919054 035 $a(OCoLC)794328357 035 $a(SSID)ssj0000654360 035 $a(PQKBManifestationID)12216668 035 $a(PQKBTitleCode)TC0000654360 035 $a(PQKBWorkID)10661709 035 $a(PQKB)10744267 035 $a(MiAaPQ)EBC919054 035 $a(WSP)00008247 035 $a(Au-PeEL)EBL919054 035 $a(CaPaEBR)ebr10563513 035 $a(CaONFJC)MIL364659 035 $a(EXLCZ)992550000000101468 100 $a20120607d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAntieigenvalue analysis$b[electronic resource] $ewith applications to numerical analysis, wavelets, statistics, quantum mechanics, finance and optimization /$fKarl Gustafson 210 $aSingapore $cWorld Scientific Pub. Co.$d2012 215 $a1 online resource (259 p.) 300 $aDescription based upon print version of record. 311 $a981-4366-28-5 320 $aIncludes bibliographical references and index. 327 $aContents; Preface; 1. Introduction; Perspective; 1.1 A Recent Referee Speaks; 1.2 The Original Motivation; 1.3 The Essential Entities; 1.4 Simple Examples and a Picture; 1.5 Applications to-Date; 1.6 Organization of this Book; Commentary; 1.7 Exercises; 2. The Original Motivation: Operator Semigroups; Perspective; 2.1 Abstract Initial Value Problems; 2.2 The Hille-Yosida-Phillips-Lumer Theorem; 2.3 The Rellich-Kato-Nelson-Gustafson Theorem; 2.4 The Multiplicative Perturbation Theorem; 2.5 When are Positive Operator Products Positive?; 2.6 Nonnegative Contraction Semigroups; Commentary 327 $a2.7 Exercises3. The Essentials of Antieigenvalue Theory; Perspective; 3.1 Convexity Properties of Norm Geometry; 3.2 The Min-Max Theorem; 3.3 The Euler Equation; 3.4 Higher Antieigenvalues and Antieigenvectors; 3.5 The Triangle Inequality; 3.6 Extended Operator Trigonometry; Commentary; 3.7 Exercises; 4. Applications in Numerical Analysis; Perspective; 4.1 Gradient Descent: Kantorovich Bound is Trigonometric; 4.2 Minimum Residual Ax = b Solvers; 4.3 Richardson Relaxation Schemes (e.g. SOR); 4.4 Very Rich Trigonometry Underlies ADI; 4.5 Domain Decomposition Multilevel Schemes 327 $a4.6 Preconditioning and Condition NumbersCommentary; 4.7 Exercises; 5. Applications in Wavelets, Control, Scattering; Perspective; 5.1 The Time Operator of Wavelets; 5.2 Frame Operator Trigonometry; 5.3 Wavelet Reconstruction is Trigonometric; 5.4 New Basis Trigonometry; 5.5 Trigonometry of Lyapunov Stability; 5.6 Multiplicative Perturbation and Irreversibility; Commentary; 5.7 Exercises; 6. The Trigonometry of Matrix Statistics; Perspective; 6.1 Statistical Efficiency; 6.2 The Euler Equation versus the Inefficiency Equation; 6.3 Canonical Correlations and Rayleigh Quotients 327 $a6.4 Other Statistics Inequalities6.5 Prediction Theory: Association Measures; 6.6 Antieigenmatrices; Commentary; 6.7 Exercises; 7. Quantum Trigonometry; Perspective; 7.1 Bell-Wigner-CHSH Inequalities; 7.2 Trigonometric Quantum Spin Identities; 7.3 Quantum Computing: Phase Issues; 7.4 Penrose Twistors; 7.5 Elementary Particles; 7.6 Trigonometry of Quantum States; Commentary; 7.7 Exercises; 8. Financial Instruments; Perspective; 8.1 Some Remarks on Mathematical Finance; 8.2 Quantos: Currency Options; 8.3 Multi-Asset Pricing: Spread Options; 8.4 Portfolio Rebalancing 327 $a8.5 American Options with Random Volatility8.6 Risk Measures for Incomplete Markets; Commentary; 8.7 Exercises; 9. Other Directions; Perspective; 9.1 Operators; 9.2 Angles; 9.3 Optimization; 9.4 Equalities; 9.5 Geometry; 9.6 Applications; Commentary; 9.7 Exercises; Appendix A Linear Algebra; A.1 Matrix Analysis; A.2 Operator Theory; Appendix B Hints and Answers to Exercises; Chapter 1.; Chapter 2.; Chapter 3.; Chapter 4.; Chapter 5.; Chapter 6.; Chapter 7.; Chapter 8.; Chapter 9.; Bibliography; Index 330 $aKarl Gustafson is the creater of the theory of antieigenvalue analysis. Its applications spread through fields as diverse as numerical analysis, wavelets, statistics, quantum mechanics, and finance. Antieigenvalue analysis, with its operator trigonometry, is a unifying language which enables new and deeper geometrical understanding of essentially every result in operator theory and matrix theory, together with their applications. 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