Abstract
In 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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References
Beck A, Teboulle M (2003) Mirror descent and nonlinear projected subgradient methods for convex optimization. Oper Res Lett 31:167–175
Nemirovski A, Yudin D (1983) Problem complexity and method efficiency in optimization. Wiley, New York
Zaslavski AJ (2016) Numerical optimization with computational errors. Springer, Cham
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J. Zaslavski, A. (2020). The Mirror Descent Algorithm. 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_3
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DOI: https://doi.org/10.1007/978-3-030-37822-6_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37821-9
Online ISBN: 978-3-030-37822-6
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