朴素
发表于 2025-3-25 05:18:57
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不开心
发表于 2025-3-25 08:47:19
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.
扩张
发表于 2025-3-25 14:54:30
https://doi.org/10.1007/978-3-663-07526-4mate solution of the problem in the presence of computational errors. It is known that the algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. In our study, presented in this book, we take into consideration the fact tha
BINGE
发表于 2025-3-25 16:30:54
https://doi.org/10.1007/978-3-663-07526-4f 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
华而不实
发表于 2025-3-25 20:04:59
Safety and Epistemic Frankfurt Cases,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
Mnemonics
发表于 2025-3-26 00:10:30
https://doi.org/10.1007/978-3-030-67572-1nvex–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
不可比拟
发表于 2025-3-26 05:28:47
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有权威
发表于 2025-3-26 09:41:18
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斜谷
发表于 2025-3-26 14:59:22
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CROW
发表于 2025-3-26 16:53:29
https://doi.org/10.1007/978-3-030-67572-1r. 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 computational errors for the two steps of our algorithm, we fin