Abstract
In this chapter, we study the algorithms for constrained convex minimization problems in a general Banach space that were described in Chap. 1. Our goal is to obtain a good approximate solution of the problem in the presence of computational errors. It is shown that the algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. We obtain a number of convergence results under different conditions including a theorem with explicit estimations for computational errors.
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Zaslavski, A.J. (2022). Convex Optimization. In: Optimization in Banach Spaces. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-031-12644-4_2
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DOI: https://doi.org/10.1007/978-3-031-12644-4_2
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Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12643-7
Online ISBN: 978-3-031-12644-4
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