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010   $a3-030-41804-9
024 7 $a10.1007/978-3-030-41804-5
035   $a(CKB)4100000011273499
035   $a(MiAaPQ)EBC6191365
035   $a(DE-He213)978-3-030-41804-5
035   $a(PPN)248396544
035   $a(EXLCZ)994100000011273499
100   $a20200505d2020      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aConvex Analysis for Optimization $eA Unified Approach /$fby Jan Brinkhuis
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (278 pages)
225 1 $aGraduate Texts in Operations Research,$x2662-6012
311   $a3-030-41803-0 
320   $aIncludes bibliographical references and index.
327   $aConvex Sets: Basic properties -- Convex Sets: Binary Operations -- Convex Sets: Topological Properties -- Convex Sets: Dual Description -- Convex Functions: Basic Properties -- Convex Functions: Dual Description -- Convex Problems: The Main Questions -- Optimality Conditions: Reformulations -- Application to Convex Problems. .
330   $aThis textbook offers graduate students a concise introduction to the classic notions of convex optimization. Written in a highly accessible style and including numerous examples and illustrations, it presents everything readers need to know about convexity and convex optimization. The book introduces a systematic three-step method for doing everything, which can be summarized as "conify, work, deconify". It starts with the concept of convex sets, their primal description, constructions, topological properties and dual description, and then moves on to convex functions and the fundamental principles of convex optimization and their use in the complete analysis of convex optimization problems by means of a systematic four-step method. Lastly, it includes chapters on alternative formulations of optimality conditions and on illustrations of their use. "The author deals with the delicate subjects in a precise yet light-minded spirit... For experts in the field, this book not only offers a unifying view, but also opens a door to new discoveries in convexity and optimization.... perfectly suited for classroom teaching." Shuzhong Zhang, Professor of Industrial and Systems Engineering, University of Minnesota.
410  0$aGraduate Texts in Operations Research,$x2662-6012
606   $aOperations research
606   $aDecision making
606   $aManagement science
606   $aMathematical optimization
606   $aConvex geometry 
606   $aDiscrete geometry
606   $aPolytopes
606   $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
606   $aContinuous Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26030
606   $aConvex and Discrete Geometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M21014
606   $aPolytopes$3https://scigraph.springernature.com/ontologies/product-market-codes/M21040
615  0$aOperations research.
615  0$aDecision making.
615  0$aManagement science.
615  0$aMathematical optimization.
615  0$aConvex geometry .
615  0$aDiscrete geometry.
615  0$aPolytopes.
615 14$aOperations Research/Decision Theory.
615 24$aOperations Research, Management Science.
615 24$aContinuous Optimization.
615 24$aConvex and Discrete Geometry.
615 24$aPolytopes.
676   $a515.642
700   $aBrinkhuis$b Jan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0772180
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910485009003321
996   $aConvex Analysis for Optimization$92013037
997   $aUNINA