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E-grāmata: Duality in Vector Optimization

  • Formāts: PDF+DRM
  • Sērija : Vector Optimization
  • Izdošanas datums: 12-Aug-2009
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642028861
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  • Formāts: PDF+DRM
  • Sērija : Vector Optimization
  • Izdošanas datums: 12-Aug-2009
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642028861
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Thecontinuousandincreasinginterestconcerningvectoroptimizationperc- tible in the research community, where contributions dealing with the theory of duality abound lately, constitutes the main motivation that led to writing this book. Decisive was also the research experience of the authors in this ?eld, materialized in a number of works published within the last decade. The need for a book on duality in vector optimization comes from the fact that despite the large amount of papers in journals and proceedings volumes, no book mainly concentrated on this topic was available so far in the scienti c landscape. There is a considerable presence of books, not all recent releases, on vector optimization in the literature. We mention here the ones due to Chen,HuangandYang(cf. [ 49]),EhrgottandGandibleux(cf. [ 65]),Eichfelder (cf. [ 66]), Goh and Yang (cf. [ 77]), G.. opfert and Nehse (cf. [ 80]), G.. opfert, - ahi, Tammer and Z? alinescu (cf. [ 81]), Jahn (cf. [ 104]), Kaliszewski (cf. [ 108]), Luc (cf. [ 125]), Miettinen (cf. [ 130]), Mishra, Wang and Lai (cf. [ 131,132]) and Sawaragi, Nakayama and Tanino (cf. [ 163]), where vector duality is at most tangentially treated. We hope that from our e orts will bene? t not only researchers interested in vector optimization, but also graduate and und- graduate students. The framework we consider is taken as general as possible, namely we work in (locally convex) topological vector spaces, going to the usual ?nite - mensional setting when this brings additional insights or relevant connections to the existing literature.

Recenzijas

From the reviews:

This book is dedicated to duality in vector optimization and is largely based on the contribution of the authors to this field. The book is divided into 7 chapters; it also contains a list of symbols and notations, an index of terms and a bibliography with 210 titles. We recommend this book to researchers in convex scalar and vector optimization.­­­ (Constantin Zlinescu, Mathematical Reviews, Issue 2010 i)

Introduction
1(8)
Preliminaries on convex analysis and vector optimization
9(54)
Convex sets
9(10)
Algebraic properties of convex sets
9(5)
Topological properties of convex sets
14(5)
Convex functions
19(11)
Algebraic properties of convex functions
19(6)
Topological properties of convex functions
25(5)
Conjugate functions and subdifferentiability
30(12)
Conjugate functions
30(8)
Subdifferentiability
38(4)
Minimal and maximal elements of sets
42(15)
Minimality
42(3)
Weak minimality
45(1)
Proper minimality
46(8)
Linear scalarization
54(3)
Vector optimization problems
57(6)
Conjugate duality in scalar optimization
63(60)
Perturbation theory and dual problems
63(10)
The general scalar optimization problem
63(3)
Optimization problems having the composition with a linear continuous mapping in the objective function
66(2)
Optimization problems with geometric and cone constraints
68(5)
Regularity conditions and strong duality
73(13)
Regularity conditions for the general scalar optimization problem
73(3)
Regularity conditions for problems having the composition with a linear continuous mapping in the objective function
76(4)
Regularity conditions for problems with geometric and cone constraints
80(6)
Optimality conditions and saddle points
86(14)
The general scalar optimization problem
86(3)
Problems having the composition with a linear continuous mapping in the objective function
89(6)
Problems with geometric and cone constraints
95(5)
The composed convex optimization problem
100(9)
A first dual problem to (PCC)
100(5)
A second dual problem to (PCC)
105(4)
Stable strong duality and formulae for conjugate functions and subdifferentials
109(14)
Stable strong duality for the general scalar optimization problem
110(1)
The composed convex optimization problem
111(3)
Problems having the composition with a linear continuous mapping in the objective function
114(3)
Problems with geometric and cone constraints
117(6)
Conjugate vector duality via scalarization
123(58)
Fenchel type vector duality
123(9)
Duality with respect to properly efficient solutions
123(7)
Duality with respect to weakly efficient solutions
130(2)
Constrained vector optimization: a geometric approach
132(7)
Duality with respect to properly efficient solutions
132(5)
Duality with respect to weakly efficient solutions
137(2)
Constrained vector optimization: a linear scalarization approach
139(20)
A general approach for constructing a vector dual problem via linear scalarization
140(4)
Vector dual problems to (PVC) as particular instances of the general approach
144(4)
The relations between the dual vector problems to (PVC)
148(5)
Duality with respect to weakly efficient solutions
153(6)
Vector duality via a general scalarization
159(14)
A general duality scheme with respect to a general scalarization
160(5)
Linear scalarization
165(1)
Maximum(-linear) scalarization
166(2)
Set scalarization
168(2)
(Semi)Norm scalarization
170(3)
Linear vector duality
173(8)
The duals introduced via linear scalarization
173(3)
Linear vector duality with respect to weakly efficient solutions
176(2)
Nakayama's geometric dual in the linear case
178(3)
Conjugate duality for vector optimization problems with finite dimensional image spaces
181(68)
Another Fenchel type vector dual problem
181(17)
Duality with respect to properly efficient solutions
182(10)
Comparisons to (DVA) and (DV)
192(2)
Duality with respect to weakly efficient solutions
194(4)
A family of Fenchel-Lagrange type vector duals
198(20)
Duality with respect to properly efficient solutions
199(10)
Duality with respect to weakly efficient solutions
209(3)
Duality for linearly constrained vector optimization problems
212(6)
Comparisons between different duals to (PVFC)
218(9)
Linear vector duality for problems with finite dimensional image spaces
227(8)
Duality with respect to properly efficient solutions
227(5)
Duality with respect to weakly efficient solutions
232(3)
Classical linear vector duality in finite dimensional spaces
235(14)
Duality with respect to efficient solutions
235(9)
Duality with respect to weakly efficient solutions
244(5)
Wolfe and Mond-Weir duality concepts
249(62)
Classical scalar Wolfe and Mond-Weir duality
249(11)
Scalar Wolfe and Mond-Weir duality: nondifferentiable case
249(2)
Scalar Wolfe and Mond-Weir duality: differentiable case
251(3)
Scalar Wolfe and Mond-Weir duality under generalized convexity hypotheses
254(6)
Classical vector Wolfe and Mond-Weir duality
260(15)
Vector Wolfe and Mond-Weir duality: nondifferentiable case
261(3)
Vector Wolfe and Mond-Weir duality: differentiable case
264(5)
Vector Wolfe and Mond-Weir duality with respect to weakly efficient solutions
269(6)
Other Wolfe and Mond-Weir type duals and special cases
275(15)
Scalar Wolfe and Mond-Weir duality without regularity conditions
276(4)
Vector Wolfe and Mond-Weir duality without regularity conditions
280(3)
Scalar Wolfe and Mond-Weir symmetric duality
283(2)
Vector Wolfe and Mond-Weir symmetric duality
285(5)
Wolfe and Mond-Weir fractional duality
290(12)
Wolfe and Mond-Weir duality in scalar fractional programming
290(4)
Wolfe and Mond-Weir duality in vector fractional programming
294(8)
Generalized Wolfe and Mond-Weir duality: a perturbation approach
302(9)
Wolfe type and Mond-Weir type duals for general scalar optimization problems
302(1)
Wolfe type and Mond-Weir type duals for different scalar optimization problems
303(3)
Wolfe type and Mond-Weir type duals for general vector optimization problems
306(5)
Duality for set-valued optimization problems based on vector conjugacy
311(74)
Conjugate duality based on efficient solutions
311(23)
Conjugate maps and the subdifferential of set-valued maps
311(8)
The perturbation approach for conjugate duality
319(11)
A special approach - vector k-conjugacy and duality
330(4)
The set-valued optimization problem with constraints
334(18)
Duality based on general vector conjugacy
335(7)
Duality based on vector k-conjugacy
342(4)
Stability criteria
346(6)
The set-valued optimization problem having the composition with a linear continuous mapping in the objective function
352(8)
Fenchel set-valued duality
352(4)
Set-valued gap maps for vector variational inequalities
356(4)
Conjugate duality based on weakly efficient solutions
360(12)
Basic notions, conjugate maps and subdifferentiability
360(6)
The perturbation approach
366(6)
Some particular instances of (PSVGw)
372(13)
The set-valued optimization problem with constraints
372(5)
The set-valued optimization problem having the composition with a linear continuous mapping in the objective map
377(2)
Set-valued gap maps for set-valued equilibrium problems
379(6)
References 385(12)
Index 397