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

Menthol

書目名稱Optimization on Solution Sets of Common Fixed Point Problems影響因子(影響力)




書目名稱Optimization on Solution Sets of Common Fixed Point Problems影響因子(影響力)學科排名




書目名稱Optimization on Solution Sets of Common Fixed Point Problems網絡公開度




書目名稱Optimization on Solution Sets of Common Fixed Point Problems網絡公開度學科排名




書目名稱Optimization on Solution Sets of Common Fixed Point Problems被引頻次




書目名稱Optimization on Solution Sets of Common Fixed Point Problems被引頻次學科排名




書目名稱Optimization on Solution Sets of Common Fixed Point Problems年度引用




書目名稱Optimization on Solution Sets of Common Fixed Point Problems年度引用學科排名




書目名稱Optimization on Solution Sets of Common Fixed Point Problems讀者反饋




書目名稱Optimization on Solution Sets of Common Fixed Point Problems讀者反饋學科排名

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.

落葉劑

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

魯莽

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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