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The mathematical model based on the linear integro-differential Boltzmann equation is considered in this article. The model describes the radiation transfer in the scattering medium irradiated by a point source. The inverse problem for the transfer equation is defined. This problem consists of determining the scattering coefficient from the time-angular distribution of the radiation flux density at a given point in space. The Neumann series representation for solving the radiation transfer equation is analyzed in the study of the inverse problem. The zero member of the series describes the unscattered radiation, the first member of the series describes a single-scattered field, the remaining members of the series describe a multiple-scattered field. When calculating the approximate solution of the radiation transfer equation, the single scattering approximation is widespread to calculated an approximate solution of the equation for regions with a small optical thickness and a low level of scattering. An analytical formula is obtained for finding the scattering coefficient by using this approximation for problem with additional restrictions on the initial data. To verify the adequacy of the obtained formula the Monte Carlo weighted method for solving the transfer equation is constructed and software implemented taking into account multiple scattering in the medium and the space-time singularity of the radiation source. As applied to the problems of high-frequency acoustic sensing in the ocean, computational experiments were carried out. The application of the single scattering approximation is justified, at least, at a sensing range of about one hundred meters and the double and triple scattered fields make the main impact on the formula error. For larger regions, the single scattering approximation gives at the best only a qualitative evaluation of the medium structure, sometimes it even does not allow to determine the order of the parameters quantitative characteristics of the interaction of radiation with matter.

In this work, the problem of constructing an electronic analogue of heterogeneous DNA is solved with the help of the methods of mathematical modeling. Electronic analogs of that type, along with other physical models of living systems, are widely used as a tool for studying the dynamic and functional properties of these systems. The solution to the problem is based on an algorithm previously developed for homogeneous (synthetic) DNA and modified in such a way that it can be used for the case of inhomogeneous (native) DNA. The algorithm includes the following steps: selection of a model that simulates the internal mobility of DNA; construction of a transformation that allows you to move from the DNA model to its electronic analogue; search for conditions that provide an analogy of DNA equations and electronic analogue equations; calculation of the parameters of the equivalent electrical circuit. To describe inhomogeneous DNA, the model was chosen that is a system of discrete nonlinear differential equations simulating the angular deviations of nitrogenous bases, and Hamiltonian corresponding to these equations. The values of the coefficients in the model equations are completely determined by the dynamic parameters of the DNA molecule, including the moments of inertia of nitrous bases, the rigidity of the sugar-phosphate chain, and the constants characterizing the interactions between complementary bases in pairs. The inhomogeneous Josephson line was used as a basis for constructing an electronic model, the equivalent circuit of which contains four types of cells: A-, T-, G-, and C-cells. Each cell, in turn, consists of three elements: capacitance, inductance, and Josephson junction. It is important that the A-, T-, G- and C-cells of the Josephson line are arranged in a specific order, which is similar to the order of the nitrogenous bases (A, T, G and C) in the DNA sequence. The transition from DNA to an electronic analog was carried out with the help of the A-transformation which made it possible to calculate the values of the capacitance, inductance, and Josephson junction in the A-cells. The parameter values for the T-, G-, and C-cells of the equivalent electrical circuit were obtained from the conditions imposed on the coefficients of the model equations and providing an analogy between DNA and the electronic model.

In this paper we propose high-order (tensor) methods for two types of saddle point problems. Firstly, we consider the classic min-max saddle point problem. Secondly, we consider the search for a stationary point of the saddle point problem objective by its gradient norm minimization. Obviously, the stationary point does not always coincide with the optimal point. However, if we have a linear optimization problem with linear constraints, the algorithm for gradient norm minimization becomes useful. In this case we can reconstruct the solution of the optimization problem of a primal function from the solution of gradient norm minimization of dual function. In this paper we consider both types of problems with no constraints. Additionally, we assume that the objective function is $\mu$-strongly convex by the first argument, $\mu$-strongly concave by the second argument, and that the $p$-th derivative of the objective is Lipschitz-continous.

For min-max problems we propose two algorithms. Since we consider strongly convex a strongly concave problem, the first algorithm uses the existing tensor method for regular convex concave saddle point problems and accelerates it with the restarts technique. The complexity of such an algorithm is linear. If we additionally assume that our objective is first and second order Lipschitz, we can improve its performance even more. To do this, we can switch to another existing algorithm in its area of quadratic convergence. Thus, we get the second algorithm, which has a global linear convergence rate and a local quadratic convergence rate.

Finally, in convex optimization there exists a special methodology to solve gradient norm minimization problems by tensor methods. Its main idea is to use existing (near-)optimal algorithms inside a special framework. I want to emphasize that inside this framework we do not necessarily need the assumptions of strong convexity, because we can regularize the convex objective in a special way to make it strongly convex. In our article we transfer this framework on convex-concave objective functions and use it with our aforementioned algorithm with a global linear convergence and a local quadratic convergence rate.

Since the saddle point problem is a particular case of the monotone variation inequality problem, the proposed methods will also work in solving strongly monotone variational inequality problems.

Stochastic optimization is a current area of research due to significant advances in machine learning and their applications to everyday problems. In this paper, we consider two fundamentally different methods for solving the problem of stochastic optimization — online and offline algorithms. The corresponding algorithms have their qualitative advantages over each other. So, for offline algorithms, it is required to solve an auxiliary problem with high accuracy. However, this can be done in a distributed manner, and this opens up fundamental possibilities such as, for example, the construction of a dual problem. Despite this, both online and offline algorithms pursue a common goal — solving the stochastic optimization problem with a given accuracy. This is reflected in the comparison of the computational complexity of the described algorithms, which is demonstrated in this paper.

The comparison of the described methods is carried out for two types of stochastic problems — convex optimization and saddles. For problems of stochastic convex optimization, the existing solutions make it possible to compare online and offline algorithms in some detail. In particular, for strongly convex problems, the computational complexity of the algorithms is the same, and the condition of strong convexity can be weakened to the condition of $\gamma$-growth of the objective function. From this point of view, saddle point problems are much less studied. Nevertheless, existing solutions allow us to outline the main directions of research. Thus, significant progress has been made for bilinear saddle point problems using online algorithms. Offline algorithms are represented by just one study. In this paper, this example demonstrates the similarity of both algorithms with convex optimization. The issue of the accuracy of solving the auxiliary problem for saddles was also worked out. On the other hand, the saddle point problem of stochastic optimization generalizes the convex one, that is, it is its logical continuation. This is manifested in the fact that existing results from convex optimization can be transferred to saddles. In this paper, such a transfer is carried out for the results of the online algorithm in the convex case, when the objective function satisfies the $\gamma$-growth condition.

Instrument of numerical-analytical modeling of characteristics of propagation of electromagnetic waves in chaotic space plasma with taking into account effects of gravitation is developed for interpretation of data of measurements of astrophysical precision instruments of new education. The task of propagation of waves in curved (Riemann’s) space is solved in Euclid’s space by introducing of the effective index of refraction of vacuum. The gravitational potential can be calculated for various model of distribution of mass of astrophysical objects and at solution of Poisson’s equation. As a result the effective index of refraction of vacuum can be evaluated. Approximate model of the effective index of refraction is suggested with condition that various objects additively contribute in total gravitational field. Calculation of the characteristics of electromagnetic waves in the gravitational field of astrophysical objects is performed by the approximation of geometrical optics with condition that spatial scales of index of refraction a lot more wavelength. Light differential equations in Euler’s form are formed the basis of numerical-analytical instrument of modeling of trajectory characteristic of waves. Chaotic inhomogeneities of space plasma are introduced by model of spatial correlation function of index of refraction. Calculations of refraction scattering of waves are performed by the approximation of geometrical optics. Integral equations for statistic moments of lateral deviations of beams in picture plane of observer are obtained. Integrals for moments are reduced to system of ordinary differential equations the firsts order with using analytical transformations for cooperative numerical calculation of arrange and meansquare deviations of light. Results of numerical-analytical modeling of trajectory picture of propagation of electromagnetic waves in interstellar space with taking into account impact of gravitational fields of space objects and refractive scattering of waves on inhomogeneities of index of refraction of surrounding plasma are shown. Based on the results of modeling quantitative estimation of conditions of stochastic blurring of the effect of gravitational lensing of electromagnetic waves at various frequency ranges is performed. It’s shown that operating frequencies of meter range of wavelengths represent conditional low-frequency limit for observational of the effect of gravitational lensing in stochastic space plasma. The offered instrument of numerical-analytical modeling can be used for analyze of structure of electromagnetic radiation of quasar propagating through group of galactic.

The article covered results of three-dimensional modeling of ecologic situation of shallow water on the example of the Azov Sea with using schemes of increased order of accuracy on multiprocessor computer system of Southern Federal University. Discrete analogs of convective and diffusive transfer operators of the fourth order of accuracy in the case of partial occupancy of cells were constructed and studied. The developed scheme of the high (fourth) order of accuracy were used for solving problems of aquatic ecology and modeling spatial distribution of polluting nutrients, which caused growth of phytoplankton, many species of which are toxic and harmful. The use of schemes of the high order of accuracy are improved the quality of input data and decreased the error in solutions of model tasks of aquatic ecology. Numerical experiments were conducted for the problem of transportation of substances on the basis of the schemes of the second and fourth orders of accuracy. They’re showed that the accuracy was increased in 48.7 times for diffusion-convection problem. The mathematical algorithm was proposed and numerically implemented, which designed to restore the bottom topography of shallow water on the basis of hydrographic data (water depth at individual points or contour level). The map of bottom relief of the Azov Sea was generated with using this algorithm. It’s used to build fields of currents calculated on the basis of hydrodynamic model. The fields of water flow currents were used as input data of the aquatic ecology models. The library of double-layered iterative methods was developed for solving of nine-diagonal difference equations. It occurs in discretization of model tasks of challenges of pollutants concentration, plankton and fish on multiprocessor computer system. It improved the precision of the calculated data and gave the possibility to obtain operational forecasts of changes in ecologic situation of shallow water in short time intervals.

It is known that the sound speed in medium that contain highly compressible inclusions, e.g. air pores in an elastic medium or gas bubbles in the liquid may be significantly reduced compared to a homogeneous medium. Effective nonlinear parameter of medium, describing the manifestation of nonlinear effects, increases hundreds and thousands of times because of the large differences in the compressibility of the inclusions and the medium. Spatial change in the concentration of such inclusions leads to the variable local sound speed, which in turn calls the spatial-temporal redistribution of acoustic energy in the wave and the distortion of its temporal profiles and cross-section structure of bounded beams. In particular, focal areas can form. Under certain conditions, the sound channel is formed that provides waveguide propagation of acoustic signals in the medium with similar inclusions. Thus, it is possible to control spatial-temporal structure of acoustic waves with the introduction of highly compressible inclusions with a given spatial distribution and concentration. The aim of this work is to study the propagation of acoustic waves in a rubberlike material with non-uniform spatial air cavities. The main objective is the development of an adequate theory of such structurally inhomogeneous media, theory of propagation of nonlinear acoustic waves and beams in these media, the calculation of the acoustic fields and identify the communication parameters of the medium and inclusions with characteristics of propagating waves. In the work the evolutionary self-consistent equation with integro-differential term is obtained describing in the low-frequency approximation propagation of intense acoustic beams in a medium with highly compressible cavities. In this equation the secondary acoustic field is taken into account caused by the dynamics of the cavities oscillations. The method is developed to obtain exact analytical solutions for nonlinear acoustic field of the beam on its axis and to calculate the field in the focal areas. The obtained results are applied to theoretical modeling of a material with non-uniform distribution of strongly compressible inclusions.

The paper considers the problem of estimating the parameters of time series described by regression models with Markov switching of two regimes at random instants of time with independent Gaussian noise. For the solution, we propose a variant of the EM algorithm based on the iterative procedure, during which an estimation of the regression parameters is performed for a given sequence of regime switching and an evaluation of the switching sequence for the given parameters of the regression models. In contrast to the well-known methods of estimating regression parameters in the models with Markov switching, which are based on the calculation of a posteriori probabilities of discrete states of the switching sequence, in the paper the estimates are calculated of the switching sequence, which are optimal by the criterion of the maximum of a posteriori probability. As a result, the proposed algorithm turns out to be simpler and requires less calculations. Computer modeling allows to reveal the factors influencing accuracy of estimation. Such factors include the number of observations, the number of unknown regression parameters, the degree of their difference in different modes of operation, and the signal-to-noise ratio which is associated with the coefficient of determination in regression models. The proposed algorithm is applied to the problem of estimating parameters in regression models for the rate of daily return of the RTS index, depending on the returns of the S&P 500 index and Gazprom shares for the period from 2013 to 2018. Comparison of the estimates of the parameters found using the proposed algorithm is carried out with the estimates that are formed using the EViews econometric package and with estimates of the ordinary least squares method without taking into account regimes switching. The account of regimes switching allows to receive more exact representation about structure of a statistical dependence of investigated variables. In switching models, the increase in the signal-to-noise ratio leads to the fact that the differences in the estimates produced by the proposed algorithm and using the EViews program are reduced.

A simplified implicit Euler method was analyzed as an alternative to the explicit Euler method, which is a commonly used method in numerical modeling in electrophysiology. The majority of electrophysiological models are quite stiff, since the dynamics they describe includes a wide spectrum of time scales: a fast depolarization, that lasts milliseconds, precedes a considerably slow repolarization, with both being the fractions of the action potential observed in excitable cells. In this work we estimate stiffness by a formula that does not require calculation of eigenvalues of the Jacobian matrix of the studied ODEs. The efficiency of the numerical methods was compared on the case of typical representatives of detailed and conceptual type models of excitable cells: Hodgkin–Huxley model of a neuron and Aliev–Panfilov model of a cardiomyocyte. The comparison of the efficiency of the numerical methods was carried out via norms that were widely used in biomedical applications. The stiffness ratio’s impact on the speedup of simplified implicit method was studied: a real gain in speed was obtained for the Hodgkin–Huxley model. The benefits of the usage of simple and high-order methods for electrophysiological models are discussed along with the discussion of one method’s stability issues. The reasons for using simplified instead of high-order methods during practical simulations were discussed in the corresponding section. We calculated higher order derivatives of the solutions of Hodgkin-Huxley model with various stiffness ratios; their maximum absolute values appeared to be quite large. A numerical method’s approximation constant’s formula contains the latter and hence ruins the effect of the other term (a small factor which depends on the order of approximation). This leads to the large value of global error. We committed a qualitative stability analysis of the explicit Euler method and were able to estimate the model’s parameters influence on the border of the region of absolute stability. The latter is used when setting the value of the timestep for simulations a priori.

Industrial imaging or “machine vision” is currently a key technology in many industries as it can be used to optimize various processes. The purpose of this work is to create a software and hardware complex for measuring the overall and weight characteristics of cargo based on an intelligent system using neural network identification methods that allow one to overcome the technological limitations of similar complexes implemented on ultrasonic and infrared measuring sensors. The complex to be developed will measure cargo without restrictions on the volume and weight characteristics of cargo to be tariffed and sorted within the framework of the warehouse complexes. The system will include an intelligent computer program that determines the volume and weight characteristics of cargo using the machine vision technology and an experimental sample of the stand for measuring the volume and weight of cargo.

We analyzed the solutions to similar problems. We noted that the disadvantages of the studied methods are very high requirements for the location of the camera, as well as the need for manual operations when calculating the dimensions, which cannot be automated without significant modifications. In the course of the work, we investigated various methods of object recognition in images to carry out subject filtering by the presence of cargo and measure its overall dimensions. We obtained satisfactory results when using cameras that combine both an optical method of image capture and infrared sensors. As a result of the work, we developed a computer program allowing one to capture a continuous stream from Intel RealSense video cameras with subsequent extraction of a three-dimensional object from the designated area and to calculate the overall dimensions of the object. At this stage, we analyzed computer vision techniques; developed an algorithm to implement the task of automatic measurement of goods using special cameras and the software allowing one to obtain the overall dimensions of objects in automatic mode.

Upon completion of the work, this development can be used as a ready-made solution for transport companies, logistics centers, warehouses of large industrial and commercial enterprises.