Titlebook: Optimization on Solution Sets of Common Fixed Point Problems; Alexander J. Zaslavski Book 2021 The Editor(s) (if applicable) and The Autho

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赦免

inveigh

Proximal Point Subgradient Algorithm,ach of them generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. Moreover, if we known computational errors for our algorithm, we find out what an approximate solution can be obtained and how many iterates one needs for this.

有害

Fixed Point Gradient Projection Algorithm,rrors. We show that an algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. Moreover, if we known computational errors for our algorithm, we find out what an approximate solution can be obtained and how many iterates one needs for this.

落叶剂

Cimmino Gradient Projection Algorithm,s. We show that an algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. Moreover, if we known computational errors for our algorithm, we find out what an approximate solution can be obtained and how many iterates one needs for this.

instill

Springer Optimization and Its Applicationshttp://image.papertrans.cn/o/image/703308.jpg

不舒服

https://doi.org/10.1007/978-3-030-78849-0subgradient algorithms; quasi-nonexpansive mapping; dynamioc string-averaging; solution sets; common fix

鲁莽

Moderate

A Class of Nonsmooth Convex Optimization Problems,this class of problems, an objective function is assumed to be convex but a set of admissible points is not necessarily convex. Our goal is to obtain an .-approximate solution in the presence of computational errors, where . is a given positive number.

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