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Totally Convex Functions for Fixed Points Computation and Infinite Dimensional Optimization (Paperback, Softcover reprint of the original 1st ed. 2000)
Loot Price: R1,476
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Totally Convex Functions for Fixed Points Computation and Infinite Dimensional Optimization (Paperback, Softcover reprint of the original 1st ed. 2000)
Series: Applied Optimization, 40
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The aim of this work is to present in a unified approach a series
of results concerning totally convex functions on Banach spaces and
their applications to building iterative algorithms for computing
common fixed points of mea surable families of operators and
optimization methods in infinite dimen sional settings. The notion
of totally convex function was first studied by Butnariu, Censor
and Reich [31] in the context of the space lRR because of its
usefulness for establishing convergence of a Bregman projection
method for finding common points of infinite families of closed
convex sets. In this finite dimensional environment total convexity
hardly differs from strict convexity. In fact, a function with
closed domain in a finite dimensional Banach space is totally
convex if and only if it is strictly convex. The relevancy of total
convexity as a strengthened form of strict convexity becomes
apparent when the Banach space on which the function is defined is
infinite dimensional. In this case, total convexity is a property
stronger than strict convexity but weaker than locally uniform
convexity (see Section 1.3 below). The study of totally convex
functions in infinite dimensional Banach spaces was started in [33]
where it was shown that they are useful tools for extrapolating
properties commonly known to belong to operators satisfying
demanding contractivity requirements to classes of operators which
are not even mildly nonexpansive.
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