24 research outputs found
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
Best Approximation from the Kuhn-Tucker Set of Composite Monotone Inclusions
Kuhn-Tucker points play a fundamental role in the analysis and the numerical
solution of monotone inclusion problems, providing in particular both primal
and dual solutions. We propose a class of strongly convergent algorithms for
constructing the best approximation to a reference point from the set of
Kuhn-Tucker points of a general Hilbertian composite monotone inclusion
problem. Applications to systems of coupled monotone inclusions are presented.
Our framework does not impose additional assumptions on the operators present
in the formulation, and it does not require knowledge of the norm of the linear
operators involved in the compositions or the inversion of linear operators