arXiv subjects

Rinat Kamalov

Publications and source records attributed to Rinat Kamalov.

5 recordsLinked to original sources

arXiv2025

Chebyshev approximation by non-Chebyshev systems

We address the problem of the best uniform approximation by linear combinations of a finite system of functions. If the system is Chebyshev and the problem is unconstrained, then the classical Remez algorithm provides a fast and precise solution. For non-Chebyshev systems, this problem may offer a great resistance. The same happens to approximations under linear constraints. We propose a solution by modifying the concept of alternance and of the Remez iterative procedure. A criterion of the best approximation is proved and the full set of polynomials of best approximation (which may not be unique in the non-Chebyshev case) is characterized. The method of finding the best polynomial is applicable for arbitrary functional systems under arbitrary linear constraints. The efficiency is demonstrated in examples with systems of complex exponents, Gaussian functions, and lacunar polynomials. As an application, the Markov-Bernstein type inequalities are obtained for those systems. Applications to signal processing, linear ODEs, switching dynamical systems are considered.

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arXiv2024

How do the lengths of switching intervals influence the stability of a dynamical system?

If a linear switching system with frequent switches is stable, will it be stable under arbitrary switches? In general, the answer is negative. Nevertheless, this question can be answered in an explicit form for any concrete system. This is done by finding the mode-dependent critical lengths of switching intervals after which any enlargement does not influence the stability. The solution is given in terms of the exponential polynomials of least deviation from zero on a segment (``Chebyshev-like'' polynomials). By proving several theoretical results on exponential polynomial approximation we derive an algorithm for finding such polynomials and for computing the critical switching time. The convergence of the algorithm is estimated and numerical results are provided.

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arXiv2022

The length of switching intervals of a stable linear system

The linear switching system is a system of ODE with the time-dependent matrix taking values from a given control matrix set. The system is (asymptotically) stable if all its trajectories tend to zero for every control function. We consider possible mode-dependent restrictions on the lengths of switching intervals which keeps the stability of the system. When the stability of trajectories with short switching intervals implies the stability of all trajectories? To answer this question we introduce the concept of "cut tail points" of linear operators and study them by the convex analysis tools. We reduce the problem to the construction of Chebyshev-type exponential polynomials, for which we derive an algorithm and present the corresponding numerical results.

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arXiv2022

Stability of linear systems with bounded switching intervals

We address the stability problem for linear switching systems with mode-dependent restrictions on the switching intervals. Their lengths can be bounded as from below (the guaranteed dwell-time) as from above. The upper bounds make this problem quite different from the classical case: a stable system can consist of unstable matrices, it may not possess Lyapunov functions, etc. We introduce the concept of Lyapunov multifunction with discrete monotonicity, which gives upper bounds for the Lyapunov exponent. Its existence as well as the existence of invariant norms are proved. Tight lower bounds are obtained in terms of a modified Berger-Wang formula over periodizable switching laws. Based on those results we develop a method of computation of the Lyapunov exponent with an arbitrary precision and analyse its efficiency in numerical results. The case when some of upper bounds can be cancelled is analysed.

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arXiv2021

Tensor methods for strongly convex strongly concave saddle point problems and strongly monotone variational inequalities

In this paper we propose three $p$-th order tensor methods for $μ$-strongly-convex-strongly-concave saddle point problems (SPP). The first method is based on the assumption of $p$-th order smoothness of the objective and it achieves a convergence rate of $O \left( \left( \frac{L_p R^{p - 1}}μ \right)^\frac{2}{p + 1} \log \frac{μR^2}{\varepsilon_G} \right)$, where $R$ is an estimate of the initial distance to the solution, and $\varepsilon_G$ is the error in terms of duality gap. Under additional assumptions of first and second order smoothness of the objective we connect the first method with a locally superlinear converging algorithm and develop a second method with the complexity of $O \left( \left( \frac{L_p R^{p - 1}}μ \right)^\frac{2}{p + 1}\log \frac{L_2 R \max \left\{ 1, \frac{L_1}μ \right\}}μ + \log \frac{\log \frac{L_1^3}{2 μ^2 \varepsilon_G}}{\log \frac{L_1 L_2}{μ^2}} \right)$. The third method is a modified version of the second method, and it solves gradient norm minimization SPP with $\tilde O \left( \left( \frac{L_p R^p}{\varepsilon_\nabla} \right)^\frac{2}{p + 1} \right)$ oracle calls, where $\varepsilon_\nabla$ is an error in terms of norm of the gradient of the objective. Since we treat SPP as a particular case of variational inequalities, we also propose three methods for strongly monotone variational inequalities with the same complexity as the described above.

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