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

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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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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

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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

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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

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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

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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