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E-raamat: Conjugate Duality in Convex Optimization

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The results presented in this book originate from the last decade research work of the author in the ?eld of duality theory in convex optimization. The reputation of duality in the optimization theory comes mainly from the major role that it plays in formulating necessary and suf cient optimality conditions and, consequently, in generatingdifferent algorithmic approachesfor solving mathematical programming problems. The investigations made in this work prove the importance of the duality theory beyond these aspects and emphasize its strong connections with different topics in convex analysis, nonlinear analysis, functional analysis and in the theory of monotone operators. The ?rst part of the book brings to the attention of the reader the perturbation approach as a fundamental tool for developing the so-called conjugate duality t- ory. The classical Lagrange and Fenchel duality approaches are particular instances of this general concept. More than that, the generalized interior point regularity conditions stated in the past for the two mentioned situations turn out to be p- ticularizations of the ones given in this general setting. In our investigations, the perturbationapproachrepresentsthestartingpointforderivingnewdualityconcepts for several classes of convex optimization problems. Moreover, via this approach, generalized MoreauRockafellar formulae are provided and, in connection with them, a new class of regularity conditions, called closedness-type conditions, for both stable strong duality and strong duality is introduced. By stable strong duality we understand the situation in which strong duality still holds whenever perturbing the objective function of the primal problem with a linear continuous functional.
Introduction 1
I Perturbation Functions and Dual Problems 9
1 A General Approach for Duality
9
2 The Problem Having the Composition with a Linear Continuous Operator in the Objective Function
14
3 The Problem with Geometric and Cone Constraints
19
4 The Composed Convex Optimization Problem
28
II Moreau–Rockafellar Formulae and Closedness-Type Regularity Conditions 35
5 Generalized Moreau–Rockafellar Formulae
35
6 Stable Strong Duality for the Composed Convex Optimization Problem
18
7 Stable Strong Duality for the Problem Having the Composition with a Linear Continuous Operator in the Objective Function
44
8 Stable Strong Duality for the Problem with Geometric and Cone Constraints
50
9 Closedness Regarding a Set
56
III Biconjugate Functions 65
10 The Biconjugate of a General Perturbation Function
65
11 Biconjugates Formulae for Different Classes of Convex Functions
68
12 The Supremum of an (Infinite) Family of Convex Functions
73
13 The Supremum of Two Convex Functions
82
IV Strong and Total Conjugate Duality 87
14 A General Closedness–Type Regularity Condition for (Only) Strong Duality
87
15 Strong Fenchel Duality
89
16 Strong Lagrange and Fenchel–Lagrange Duality
93
17 Total Lagrange and Fenchel–Lagrange Duality
99
V Unconventional Fenchel Duality 105
18 Totally Fenchel Unstable Functions
105
19 Totally Fenchel Unstable Functions in Finite Dimensional Spaces
112
20 Quasi Interior and Quasi-relative Interior
115
21 Regularity Conditions via qi and qri
119
22 Lagrange Duality via Fenchel Duality
127
VI Applications of the Duality to Monotone Operators 133
23 Monotone Operators and Their Representative Functions
133
24 Maximal Monotonicity of the Operator S /PTA
136
25 The Maximality of 'PTA and S T
142
26 Enlargements of Monotone Operators
148
References 157
Index 163