Gradient Algorithm with a Smooth Objective Function

Alexander Zaslavski
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Alexander Zaslavski: Israel Institute of Technology

Chapter Chapter 4 in Convex Optimization with Computational Errors, 2020, pp 127-150 from Springer

Abstract: Abstract In this chapter we analyze the convergence of a projected gradient algorithm with a smooth objective function 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 gradient of the objective function while in the second one we calculate a projection on the feasible set. In each of these two steps there is a computational error. In general, these two computational errors are different.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-37822-6_4

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DOI: 10.1007/978-3-030-37822-6_4

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