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.
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References
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J. Zaslavski, A. (2020). Gradient Algorithm with a Smooth Objective Function. In: Convex Optimization with Computational Errors. Springer Optimization and Its Applications, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-37822-6_4
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DOI: https://doi.org/10.1007/978-3-030-37822-6_4
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-37822-6
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