Elena N. Gryazina

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This paper introduces a new Matlab package, Ract, aimed at solving a class of probabilistic analysis and synthesis problems arising in control. The package offers a convenient way for defining various types of structured uncertainties as well as formulating and analyzing the ensuing robustness analysis tasks from a probabilistic point of view. It also(More)
New randomized algorithms for stabilization and optimal control for linear systems are proposed. They are based on Hit-and-Run method, which allows generating random points in convex or nonconvex domains. These domains are either stability domain in the space of feedback controllers, or quadratic stability domain, or robust stability domain, or level set(More)
We address randomized methods for control and optimization based on generating points uniformly distributed in a set. For control systems this sets are either stability domain in the space of feedback controllers, or quadratic stability domain, or robust stability domain, or level set for a performance specification. By generating random points in the(More)
In previous works the authors proposed to use Hit-and-Run versions of Markov-chain Monte-Carlo algorithms for various problems of control and optimization. In this paper we focus on robust stabilization applications of the method. The crucial notion for this algorithm is a Boundary Oracle (BO), and we start with constructing BO for robustness problems,(More)
In previous works the authors proposed to use Hit-and-Run (H&R) versions of Markov Chain Monte Carlo (MCMC) algorithms for various problems of control and optimization. However the results are unsatisfactory for ”bad” sets, such as level sets of ill-posed functions. The idea of the present paper is to exploit the technique developed for interior-point(More)
Hit-and-Run is known to be one of the best random sampling algorithms, its mixing time is polynomial in dimension. Nevertheless, in practice the number of steps required to achieve uniformly distributed samples is rather high. We propose new random walk algorithm based on billiard trajectories. Numerical experiments demonstrate much faster convergence to(More)
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