966 research outputs found
Application of Tikhonov Regularized Methods to Image Deblurring Problem
We consider the monotone inclusion problems in real Hilbert spaces. Proximal
splitting algorithms are very popular technique to solve it and generally
achieve weak convergence under mild assumptions. Researchers assume strong
conditions like strong convexity or strong monotonicity on the considered
operators to prove strong convergence of the algorithms. Mann iteration method
and normal S-iteration method are popular methods to solve fixed point
problems. We propose a new common fixed point algorithm based on normal
S-iteration method {using Tikhonov regularization }to find common fixed point
of nonexpansive operators and prove strong convergence of the generated
sequence to the set of common fixed points without assuming strong convexity
and strong monotonicity. Based on the proposed fixed point algorithm, we
propose a forward-backward-type algorithm and a Douglas-Rachford algorithm in
connection with Tikhonov regularization to find the solution of monotone
inclusion problems. Further, we consider the complexly structured monotone
inclusion problems which are very popular these days. We also propose a
strongly convergent forward-backward-type primal-dual algorithm and a
Douglas-Rachford-type primal-dual algorithm to solve the monotone inclusion
problems. Finally, we conduct a numerical experiment to solve image deblurring
problems
A weakly convergent fully inexact Douglas-Rachford method with relative error tolerance
Douglas-Rachford method is a splitting algorithm for finding a zero of the
sum of two maximal monotone operators. Each of its iterations requires the
sequential solution of two proximal subproblems. The aim of this work is to
present a fully inexact version of Douglas-Rachford method wherein both
proximal subproblems are solved approximately within a relative error
tolerance. We also present a semi-inexact variant in which the first subproblem
is solved exactly and the second one inexactly. We prove that both methods
generate sequences weakly convergent to the solution of the underlying
inclusion problem, if any
The Cyclic Douglas-Rachford Method for Inconsistent Feasibility Problems
We analyse the behaviour of the newly introduced cyclic Douglas-Rachford
algorithm for finding a point in the intersection of a finite number of closed
convex sets. This work considers the case in which the target intersection set
is possibly empty.Comment: 13 pages, 2 figures; references updated, figure 2 correcte
Global Behavior of the Douglas-Rachford Method for a Nonconvex Feasibility Problem
In recent times the Douglas-Rachford algorithm has been observed empirically
to solve a variety of nonconvex feasibility problems including those of a
combinatorial nature. For many of these problems current theory is not
sufficient to explain this observed success and is mainly concerned with
questions of local convergence. In this paper we analyze global behavior of the
method for finding a point in the intersection of a half-space and a
potentially non-convex set which is assumed to satisfy a well-quasi-ordering
property or a property weaker than compactness. In particular, the special case
in which the second set is finite is covered by our framework and provides a
prototypical setting for combinatorial optimization problems
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