Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches

Rogozin, Alexander; Yarmoshik, Demyan; Kopylova, Ksenia; Gasnikov, Alexander

doi:10.1007/978-3-031-22990-9_7

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…About 3000 parameters of the mirror surfaces are adjusted, during focusing of the mirror system.The faster setting up of the mirrors is very important for the accuracy of observations. For acceleration, it is proposed to use the Nesterov acceleration method in distributed form [16][17][18]. In Figure 3 on page 11, the dependence of the signal power on the number of iterations of the algorithm is given.…”

Section: Application For Orientation Improvement Of Radio Telescope's...mentioning

confidence: 99%

Dynamic System Identification via Randomized Stochastic Optimization Under Unknown-but-Bounded Noise

Granichin,

Ivanskiy,

Kopylova

2023

Preprint

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Discretization in time and in the state space of the system leads to the necessity to solve the parameter identification problems for dynamic systems in a limited time (at a finite time interval) using observations obtained under the influence of unknown-but-bounded noise. Finding the solution in this case is more difficult compared to traditional identification problem setting which considers random independent zero-mean noise. For system parameter identification problem under unknown-but-bounded noise, a randomized stochastic optimization algorithm is given in the paper, estimates for the mean square values of the residuals for a finite observation interval are obtained. An example of application of the given method to the problem of tuning the parameters of a multi-mirror telescope is considered.

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Section: Application For Orientation Improvement Of Radio Telescope's...mentioning

confidence: 99%

Dynamic System Identification via Randomized Stochastic Optimization Under Unknown-but-Bounded Noise

Granichin,

Ivanskiy,

Kopylova

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the special case, when R(x, y) = x T By, (1) has been also widely studied, dating at least to the classic work of [6] (imaging inverse problems). Modern applications can be find in decentralized optimization [34,8]. Quadratic variant of the problem (1) also appeared in reinforcement learning [10].…”

Section: Bilinear Saddle Point Problemsmentioning

confidence: 99%