Adaptive Catalyst for Smooth Convex Optimization

A. Ivanova; Pasechnyuk D.; Grishchenko D.; Shulgin E.; A. Gasnikov; Matyukhin V.

doi:10.1007/978-3-030-91059-4_2

Публикации

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Adaptive Catalyst for Smooth Convex Optimization

Ch. 268319. P. 20–37.

Иванова А. С., Pasechnyuk D., Grishchenko D., Shulgin E., Гасников А. В., Matyukhin V.

Язык: английский

DOI

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Ключевые слова: Alternating minimization Adaptive Methods Accelerated methods Catalyst Coordinate descent Distributed methods

В книге

Optimization and Applications: 12th International Conference, OPTIMA 2021, Petrovac, Montenegro, September 27 – October 1, 2021, Proceedings

Switzerland: Springer, 2021.

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The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of eﬃcient methods for building approximations. However, recent results reveal the potential of low rank approximations in Chebyshev norm, which naturally arises in many applications. In this paper ...

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Convergence of an alternating maximization procedure

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We derive two convergence results for a sequential alternating maximization procedure to approximate the maximizer of random functionals such as the realized log likelihood in MLE estimation. We manage to show that the sequence attains the same deviation properties as shown for the profile M-estimator by Andresen and Spokoiny (2013), that means a finite sample ...

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