Titlebook: Convex Optimization with Computational Errors; Alexander J. Zaslavski Book 2020 Springer Nature Switzerland AG 2020 convex optimization.ma

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书目名称Convex Optimization with Computational Errors影响因子(影响力)




书目名称Convex Optimization with Computational Errors影响因子(影响力)学科排名




书目名称Convex Optimization with Computational Errors网络公开度




书目名称Convex Optimization with Computational Errors网络公开度学科排名




书目名称Convex Optimization with Computational Errors被引频次




书目名称Convex Optimization with Computational Errors被引频次学科排名




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书目名称Convex Optimization with Computational Errors年度引用学科排名




书目名称Convex Optimization with Computational Errors读者反馈




书目名称Convex Optimization with Computational Errors读者反馈学科排名

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Subgradient Projection Algorithm,f convex–concave functions, under the presence of computational errors. The problem is described by an objective function and a set of feasible points. For this algorithm each iteration consists of two steps. The first step is a calculation of a subgradient of the objective function while in the sec

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Gradient Algorithm with a Smooth Objective Function,rs. The problem is described by an objective function and a set of feasible points. For this algorithm each iteration consists of two steps. The first step is a calculation of a gradient of the objective function while in the second one we calculate a projection on the feasible set. In each of these

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Continuous Subgradient Method,nvex–concave functions, under the presence of computational errors. The problem is described by an objective function and a set of feasible points. For this algorithm we need a calculation of a subgradient of the objective function and a calculation of a projection on the feasible set. In each of th

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PDA-Based Method for Convex Optimization, steps. In each of these two steps there is a computational error. In general, these two computational errors are different. We show that our algorithm generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know the

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A Projected Subgradient Method for Nonsmooth 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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Convex Optimization with Computational Errors978-3-030-37822-6Series ISSN 1931-6828 Series E-ISSN 1931-6836