Inverse Problems · 2006 · 419 citations · 23 references
Natural SciencesComputer ScienceMathematicsAlgorithmsOptimizationSet TheoryConvex AnalysisConvex OptimizationNonlinear ProgrammingNumerical AlgorithmsBounded Linear OperatorsConvex Feasibility ProblemsNonexpansive Mappings
The variable Krasnosel'skii–Mann algorithm generates a sequence {x_n} via xn+1 = (1−α_n)x_n + α_nT_nx_n, where {α_n}⊂[0,1] and {T_n} are nonexpansive mappings, and the multiple‑set split feasibility problem seeks a point x in the intersection of convex sets whose image under a bounded linear operator lies in the intersection of another family of convex sets. The authors aim to prove weak convergence of the generated sequence in a general Banach space and to introduce additional iterative algorithms for solving the multiple‑set split feasibility problem in infinite‑dimensional Hilbert spaces. They employ the convergence result to construct iterative schemes that solve the split feasibility problem, which requires finding x∈C with Ax∈Q, and extend these schemes to the multiple‑set case where x∈∩C_i and Ax∈∩Q_j.
A variable Krasnosel'skii–Mann algorithm generates a sequence {xn} via the formula xn+1 = (1 − αn)xn + αnTnxn, where {αn} is a sequence in [0, 1] and {Tn} is a sequence of nonexpansive mappings. We will show, in a fairly general Banach space, that the sequence {xn} generated converges weakly. This result is used to solve the split feasibility problem which is to find a point x with the property that x ∊ C and Ax ∊ Q, where C and Q are closed convex subsets of Hilbert spaces H1 and H2, respectively, and A is a bounded linear operator from H1 to H2. The multiple-set split feasibility problem recently introduced by Censor et al is stated as finding a point x ∊ ∩Ni=1Ci such that Ax ∊ ∩Mj=1Qj, where N and M are positive integers, {C1, ..., CN} and {Q1, ..., QM} are closed convex subsets of H1 and H2, respectively, and A is again a linear bounded operator from H1 to H2. One of the purposes of this paper is to introduce more iterative algorithms that solve this problem in the framework of infinite-dimensional Hilbert spaces.
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Mean value methods in iteration
W. Robert Mann · Proceedings of the American Mathematical Society · 1953 · 2.3K citations
On Projection Algorithms for Solving Convex Feasibility Problems
Heinz H. Bauschke, Jonathan M. Borwein · SIAM Review · 1996 · 1.7K citations
Computing, Mathematics, Algorithms +11
Iterative Algorithms for Nonlinear Operators
Hong‐Kun Xu · Journal of the London Mathematical Society · 2002 · 1.7K citations