Duality for Nonconvex Approximation and Optimization
The theory of convex optimization has been developing constantly over the past 30 years. Recently, researchers have been studying more complicated classes of problems that still can be studied by means of convex analysis, so-called "anticonvex" and "convex-anticonvex" optimization problems. This monograph contains an exhaustive presentation of the duality theory for these classes of problems and their generalizations.
In this monograph the author presents the theory of duality for
nonconvex approximation in normed linear spaces and nonconvex global
optimization in locally convex spaces. Key topics include:
_ duality for worst approximation (i.e., the maximization of the
distance of an element to a convex set)
_ duality for reverse convex best approximation (i.e., the minimization of
the distance of an element to the complement of a convex set)
_ duality for convex maximization (i.e., the maximization of a convex
function on a convex set)
_ duality for reverse convex minimization (i.e., the minimization of a
convex function on the complement of a convex set)
_ duality for d.c. optimization (i.e., optimization problems involving
differences of convex functions).
Detailed proofs of results are given, along with varied illustrations.
While many of the results have been published in mathematical journals,
this is the first time these results appear in book form. In
addition, unpublished results and new proofs are provided. This
monograph should be of great interest to experts in this and related
fields.
Ivan Singer is a Research Professor at the Simion Stoilow Institute of
Mathematics in Bucharest, and a Member of the Romanian Academy. He is
one of the pioneers of approximation theory in normed linear spaces, and
of generalizations of approximation theory to optimization theory. He
has been a Visiting Professor at several universities in the U.S.A.,
Great Britain, Germany, Holland, Italy, and other countries, and was the
principal speaker at an N. S. F. Regional Conference at Kent State
University. He is one of the editors of the journals Numerical
Functional Analysis and Optimization (since its inception in 1979),
Optimization, and Revue d'analyse num'erique et de th'eorie de
l'approximation. His previous books include Best Approximation in
Normed Linear Spaces by Elements of Linear Subspaces (Springer 1970),
The Theory of Best Approximation and Functional Analysis (SIAM 1974), Bases
in Banach Spaces I, II (Springer, 1970, 1981), and Abstract Convex Analysis
(Wiley-Interscience, 1997).
nonconvex approximation in normed linear spaces and nonconvex global
optimization in locally convex spaces. Key topics include:
_ duality for worst approximation (i.e., the maximization of the
distance of an element to a convex set)
_ duality for reverse convex best approximation (i.e., the minimization of
the distance of an element to the complement of a convex set)
_ duality for convex maximization (i.e., the maximization of a convex
function on a convex set)
_ duality for reverse convex minimization (i.e., the minimization of a
convex function on the complement of a convex set)
_ duality for d.c. optimization (i.e., optimization problems involving
differences of convex functions).
Detailed proofs of results are given, along with varied illustrations.
While many of the results have been published in mathematical journals,
this is the first time these results appear in book form. In
addition, unpublished results and new proofs are provided. This
monograph should be of great interest to experts in this and related
fields.
Ivan Singer is a Research Professor at the Simion Stoilow Institute of
Mathematics in Bucharest, and a Member of the Romanian Academy. He is
one of the pioneers of approximation theory in normed linear spaces, and
of generalizations of approximation theory to optimization theory. He
has been a Visiting Professor at several universities in the U.S.A.,
Great Britain, Germany, Holland, Italy, and other countries, and was the
principal speaker at an N. S. F. Regional Conference at Kent State
University. He is one of the editors of the journals Numerical
Functional Analysis and Optimization (since its inception in 1979),
Optimization, and Revue d'analyse num'erique et de th'eorie de
l'approximation. His previous books include Best Approximation in
Normed Linear Spaces by Elements of Linear Subspaces (Springer 1970),
The Theory of Best Approximation and Functional Analysis (SIAM 1974), Bases
in Banach Spaces I, II (Springer, 1970, 1981), and Abstract Convex Analysis
(Wiley-Interscience, 1997).
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