Results 21 to 30 of about 1,574 (118)
Iterative algorithms with seminorm‐induced oblique projections
Abstract and Applied Analysis, Volume 2003, Issue 7, Page 387-406, 2003., 2003 A definition of oblique projections onto closed convex sets that use seminorms induced by diagonal matrices which may have zeros on the diagonal is introduced. Existence and uniqueness of such projections are secured via directional affinity of the sets with respect to the diagonal matrices involved. A block‐iterative algorithmic scheme for solving the
Yair Censor, Tommy Elfving
wiley +1 more source
EURO Journal on Computational Optimization, 2016 We analyze the proximal alternating linearized minimization algorithm (PALM) for solving non-smooth convex minimization problems where the objective function is a sum of a smooth convex function and block separable non-smooth extended real-valued convex ...
Ron Shefi, Marc Teboulle
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, 2014 In this paper, it is our purpose to introduce an iterative process for the approximation of a common fixed point of a finite family of multi-valued Bregman relatively nonexpansive mappings.
N. Shahzad, H. Zegeye
semanticscholar +1 more source
Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization [PDF]
, 2015 In this paper we analyze several inexact fast augmented Lagrangian methods for solving linearly constrained convex optimization problems. Mainly, our methods rely on the combination of excessive-gap-like smoothing technique developed in [15] and the ...
Necoara, Ion +2 more
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A global method for some class of optimization and control problems
International Journal of Mathematics and Mathematical Sciences, Volume 23, Issue 9, Page 605-616, 2000., 2000 The problem of maximizing a nonsmooth convex function over an arbitrary set is considered. Based on the optimality condition obtained by Strekalovsky in 1987 an algorithm for solving the problem is proposed. We show that the algorithm can be applied to the nonconvex optimal control problem as well.
R. Enkhbat
wiley +1 more source
Strong convergence of a relaxed CQ algorithm for the split feasibility problem
, 2013 The split feasibility problem (SFP) is finding a point in a given closed convex subset of a Hilbert space such that its image under a bounded linear operator belongs to a given closed convex subset of another Hilbert space.
Songnian He, Ziyi Zhao
semanticscholar +1 more source
A proximal point method for nonsmooth convex optimization problems in Banach spaces
Abstract and Applied Analysis, Volume 2, Issue 1-2, Page 97-120, 1997., 1997 In this paper we show the weak convergence and stability of the proximal point method when applied to the constrained convex optimization problem in uniformly convex and uniformly smooth Banach spaces. In addition, we establish a nonasymptotic estimate of convergence rate of the sequence of functional values for the unconstrained case.
Y. I. Alber, R. S. Burachik, A. N. Iusem
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On representations of the feasible set in convex optimization [PDF]
, 2009 We consider the convex optimization problem $\min \{f(x) : g_j(x)\leq 0, j=1,...,m\}$ where $f$ is convex, the feasible set K is convex and Slater's condition holds, but the functions $g_j$ are not necessarily convex.
A. Ben-Tal +8 more
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General algorithm and sensitivity analysis for variational inequalities
International Journal of Stochastic Analysis, Volume 5, Issue 1, Page 29-41, 1992., 1991 The fixed point technique is used to prove the existence of a solution for a class of variational inequalities related to odd order boundary value problems, and to suggest a general algorithm. We also study the sensitivity analysis for these variational inequalities and complementarity problems using the projection technique.
Muhammad Aslam Noor
wiley +1 more source
Uncontrolled inexact information within bundle methods
EURO Journal on Computational Optimization, 2017 We consider convex non-smooth optimization problems where additional information with uncontrolled accuracy is readily available. It is often the case when the objective function is itself the output of an optimization solver, as for large-scale energy ...
Jérôme Malick +2 more
doaj +1 more source