LEADER 03530nam 22005535 450 001 9910863146103321 005 20260810153221.0 010 $a3-030-60300-8 024 7 $a10.1007/978-3-030-60300-7 035 $a(CKB)4100000011610353 035 $a(MiAaPQ)EBC6413330 035 $a(DE-He213)978-3-030-60300-7 035 $a(PPN)252508491 035 $a(EXLCZ)994100000011610353 100 $a20201125d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Projected Subgradient Algorithm in Convex Optimization /$fby Alexander J. Zaslavski 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (VI, 146 p.) 225 1 $aSpringerBriefs in Optimization,$x2191-575X 311 08$a3-030-60299-0 320 $aIncludes bibliographical references. 327 $a1. Introduction -- 2. Nonsmooth Convex Optimization -- 3. Extensions -- 4. Zero-sum Games with Two Players -- 5. Quasiconvex Optimization -- References. 330 $aThis focused monograph presents a study of subgradient algorithms for constrained minimization problems in a Hilbert space. The book is of interest for experts in applications of optimization to engineering and economics. The goal is to obtain a good approximate solution of the problem in the presence of computational errors. The discussion takes into consideration the fact that for every algorithm its iteration consists of several steps and that computational errors for di?erent steps are di?erent, in general. The book is especially useful for the reader because it contains solutions to a number of difficult and interesting problems in the numerical optimization. The subgradient projection algorithm is one of the most important tools in optimization theory and its applications. An optimization problem is described by an objective function and a set of feasible points. For this algorithm each iteration consists of two steps. The first step requires a calculation of a subgradient of the objective function; the second requires a calculation of a projection on the feasible set. The computational errors in each of these two steps are different. This book shows that the algorithm discussed, generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if computational errors for the two steps of the algorithm are known, one discovers an approximate solution and how many iterations one needs for this. In addition to their mathematical interest, the generalizations considered in this book have a significant practical meaning. 410 0$aSpringerBriefs in Optimization,$x2191-575X 606 $aMathematical optimization 606 $aCalculus of variations 606 $aNumerical analysis 606 $aCalculus of Variations and Optimization 606 $aNumerical Analysis 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aNumerical analysis. 615 14$aCalculus of Variations and Optimization. 615 24$aNumerical Analysis. 676 $a519.6 700 $aZaslavski$b Alexander J.$0721713 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910863146103321 996 $aThe projected subgradient algorithm in convex optimization$92217879 997 $aUNINA LEADER 02388nam 2200517Ia 450 001 9911133138003321 005 20251116193113.0 010 $a0-89556-509-9 035 $a(CKB)2550000001135201 035 $a(EBL)661015 035 $a(OCoLC)705538232 035 $a(MiAaPQ)EBC661015 035 $a(Au-PeEL)EBL661015 035 $a(CaPaEBR)ebr10451001 035 $a(EXLCZ)992550000001135201 100 $a19940519d1994 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCharacter and neurosis $ean integrative view /$fClaudio Naranjo 205 $a2nd ed. 210 $aNevada City, CA $cGateways/IDHHB$dc1994 215 $a1 online resource (349 p.) 300 $aDescription based upon print version of record. 311 08$a0-89556-066-6 311 08$a1-306-03658-5 320 $aIncludes bibliographical references and index. 327 $aFront Cover ; Copyright ; Acknowledgments ; Table of Contents ; Preface by Frank Barron ; Author's Forward ; By Way of Introduction: A Theoretical Panorama ; 1. Anger and Perfectionism (Type 1) ; 2. Avarice and Pathological Detachment (Type 5) ; 3. Envy and Depressive Masochistic Character (Type 4) ; 4. Sadistic Character and Lust (Type 8) ; 5. Gluttony, Fraudulence, and ""Narcissistic Personality"" (Type 7) ; 6. Pride and the Histrionic Personality (Type 2) ; 7. Vanity, Inauthenticity, and ""the Marketing Orientation"" (Type 3) ; 8. Cowardice, Paranoid Character, and Accusation (Type 6) 327 $a9. Psychospiritual Inertia and the Over-adjusted Disposition (Type 9) 10. Suggestions for Further Work on Self ; Appendix-Remarks for Differential Diagnosis ; Biographical Notes ; Index ; Dear Reader of Character and Neurosis 330 $a