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

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https://doi.org/10.1007/978-3-030-67572-1this 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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Springer Optimization and Its Applicationshttp://image.papertrans.cn/c/image/237847.jpg

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https://doi.org/10.1007/978-94-007-5934-3In this chapter we analyze the mirror descent algorithm for minimization of convex and nonsmooth functions and for computing the saddle points of convex–concave functions, under the presence of computational errors. The problem is described by an objective function and a set of feasible points.

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Minimization of Sharp Weakly Convex Functions,In this chapter we study the subgradient projection algorithm for minimization of sharp weakly convex functions, under the presence of computational errors. The problem is described by an objective function and a set of feasible points.

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https://doi.org/10.1007/978-3-030-37822-6convex optimization; mathematical programming; computational error; nonlinear analysis; solving real-wor