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

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

A Modified Algorithm for Solving the Split Feasibility Problem

In this paper a modified algorithm for solving the split feasibility problem (SFP) is presented. This algorithm uses the generalized Armijo line search in computing predictor step size and gives a correction step rule in the iterative process, which makes an accelerated convergence to the solution of SFP. Meanwhile, it needs not to compute the matrix inverses and the large eigenvalue of the mat...

the algorithm for solving the inverse numerical range problem

برد عددی ماتریس مربعی a را با w(a) نشان داده و به این صورت تعریف می کنیم w(a)={x8ax:x ?s1} ، که در آن s1 گوی واحد است. در سال 2009، راسل کاردن مساله برد عددی معکوس را به این صورت مطرح کرده است : برای نقطه z?w(a)، بردار x?s1 را به گونه ای می یابیم که z=x*ax، در این پایان نامه ، الگوریتمی برای حل مساله برد عددی معکوس ارانه می دهیم.

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.

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...