The Split Feasibility Problem with Some Projection Methods in Banach Spaces

Regularized Methods for the Split Feasibility Problem

and Applied Analysis 3 However, 1.8 is, in general, ill posed. So regularization is needed. We consider Tikhonov’s regularization min x∈C fα : 1 2 ∥I − PQ ) Ax ∥∥2 1 2 α‖x‖, 1.9 where α > 0 is the regularization parameter. We can compute the gradient ∇fα of fα as ∇fα ∇f x αI A∗ ( I − PQ ) A αI. 1.10 Define a Picard iterates x n 1 PC ( I − γA∗I − PQ ) A αI )) x n 1.11 Xu 20 shown that if the SFP...

Modified Halfspace-Relaxation Projection Methods for Solving the Split Feasibility Problem

Self-Adaptive and Relaxed Self-Adaptive Projection Methods for Solving the Multiple-Set Split Feasibility Problem

and Applied Analysis 3 half-spaces, so that the algorithm is implementable. We need not estimate the Lipschitz constant and make a sufficient decrease of the objection function at each iteration; besides, these projection algorithms can reduce to the modifications of the CQ algorithm 6 when the MSSFP 1.1 is reduced to the SFP. We also show convergence the algorithms under mild conditions.

Iterative methods of strong convergence theorems for the split feasibility problem in Hilbert spaces

In this paper, we propose several new iterative algorithms to solve the split feasibility problem in the Hilbert spaces. By virtue of new analytical techniques, we prove that the iterative sequence generated by these iterative procedures converges to the solution of the split feasibility problem which is the best close to a given point. In particular, the minimum-norm solution can be found via ...

The retraction constant in some Banach spaces

We give new sharper estimations for the retraction constant in some Banach spaces. r 2002 Elsevier Science (USA). All rights reserved.