强单调和Lipschitz映射的零的新的Krasnoselskii型算法

M. Sène, M. Ndiaye, N. Djitté
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引用次数: 1

摘要

当q > 1时,设E是一个具有对偶空间E *的q均匀光滑实巴拿赫空间。设A: E→E∗是一个Lipschitz和强单调映射,使得A^{−1}(0)h =∅。对于给定的x_1∈E,设{x_n}由以下算法迭代生成:x_{n+1} = x_n−λJ^{−1}(Ax_n), n≥1,其中J是E到E *的归一化对偶映射,λ是在合适区间内选择的正实数。然后证明了序列{xn}强收敛于A^{−1}(0)的唯一点x *。我们的定理应用于凸极小化问题。此外,我们的证明技术是独立的。”
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"A new Krasnoselskii’s type algorithm for zeros of strongly monotone and Lipschitz mappings"
"For q > 1, let E be a q-uniformly smooth real Banach space with dual space E∗. Let A : E → E∗ be a Lipschitz and strongly monotone mapping such that A^{−1}(0) ̸= ∅. For given x_1 ∈ E, let {x_n} be generated iteratively by the algorithm : x_{n+1} = x_n − λJ^{−1}(Ax_n), n ≥ 1, where J is the normalized duality mapping from E into E∗ and λ is a positive real number choosen in a suitable interval. Then it is proved that the sequence {xn} converges strongly to x∗, the unique point of A^{−1}(0). Our theorems are applied to the convex minimization problem. Futhermore, our technique of proof is of independent interest."
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