Fixate 发表于 2025-3-21 16:29:14
书目名称Convex Optimization with Computational Errors影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0237847<br><br> <br><br>书目名称Convex Optimization with Computational Errors读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0237847<br><br> <br><br>黄油没有 发表于 2025-3-21 23:24:06
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认识 发表于 2025-3-22 01:39:12
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设想 发表于 2025-3-22 04:43:17
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 thfigurine 发表于 2025-3-22 09:04:28
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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吊胃口 发表于 2025-3-22 22:56:58
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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.Arb853 发表于 2025-3-23 08:50:44
Convex Optimization with Computational Errors978-3-030-37822-6Series ISSN 1931-6828 Series E-ISSN 1931-6836