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The paper considers the possibility of comparing and classifying dynamical systems based on topological data analysis. Determining the measures of interaction between the channels of dynamic systems based on the HIIA (Hankel Interaction Index Array) and PM (Participation Matrix) methods allows you to build HIIA and PM graphs and their adjacency matrices. For any linear dynamic system, an approximating directed graph can be constructed, the vertices of which correspond to the components of the state vector of the dynamic system, and the arcs correspond to the measures of mutual influence of the components of the state vector. Building a measure of distance (proximity) between graphs of different dynamic systems is important, for example, for identifying normal operation or failures of a dynamic system or a control system. To compare and classify dynamic systems, weighted directed graphs corresponding to dynamic systems are preliminarily formed with edge weights corresponding to the measures of interaction between the channels of the dynamic system. Based on the HIIA and PM methods, matrices of measures of interaction between the channels of dynamic systems are determined. The paper gives examples of the formation of weighted directed graphs for various dynamic systems and estimation of the distance between these systems based on topological data analysis. An example of the formation of a weighted directed graph for a dynamic system corresponding to the control system for the components of the angular velocity vector of an aircraft, which is considered as a rigid body with principal moments of inertia, is given. The method of topological data analysis used in this work to estimate the distance between the structures of dynamic systems is based on the formation of persistent barcodes and persistent landscape functions. Methods for comparing dynamic systems based on topological data analysis can be used in the classification of dynamic systems and control systems. The use of traditional algebraic topology for the analysis of objects does not allow obtaining a sufficient amount of information due to a decrease in the data dimension (due to the loss of geometric information). Methods of topological data analysis provide a balance between reducing the data dimension and characterizing the internal structure of an object. In this paper, topological data analysis methods are used, based on the use of Vietoris-Rips and Dowker filtering to assign a geometric dimension to each topological feature. Persistent landscape functions are used to map the persistent diagrams of the method of topological data analysis into the Hilbert space and then quantify the comparison of dynamic systems. Based on the construction of persistent landscape functions, we propose a comparison of graphs of dynamical systems and finding distances between dynamical systems. For this purpose, weighted directed graphs corresponding to dynamical systems are preliminarily formed. Examples of finding the distance between objects (dynamic systems) are given.

We consider a finite-dimensional optimization problem, the formulation of which in addition to the required variables contains parameters. The solution to this problem is a dependence of optimal values of variables on parameters. In general, these dependencies are not functions because they can have ambiguous meanings and in the functional case be nondifferentiable. In addition, their domain of definition may be narrower than the domains of definition of functions in the condition of the original problem. All these properties make it difficult to solve both the original parametric problem and other tasks, the statement of which includes these dependencies. To overcome these difficulties, usually methods such as non-differentiable optimization are used.

This article proposes an alternative approach that makes it possible to obtain solutions to parametric problems in a form devoid of the specified properties. It is shown that such representations can be explored using standard algorithms, based on the Taylor formula. This form is a function smoothly approximating the solution of the original problem for any parameter values, specified in its statement. In this case, the value of the approximation error is controlled by a special parameter. Construction of proposed approximations is performed using special functions that establish feedback (within optimality conditions for the original problem) between variables and Lagrange multipliers. This method is described for linear problems with subsequent generalization to the nonlinear case.

From a computational point of view the construction of the approximation consists in finding the saddle point of the modified Lagrange function of the original problem. Moreover, this modification is performed in a special way using feedback functions. It is shown that the necessary conditions for the existence of such a saddle point are similar to the conditions of the Karush – Kuhn – Tucker theorem, but do not contain constraints such as inequalities and conditions of complementary slackness. Necessary conditions for the existence of a saddle point determine this approximation implicitly. Therefore, to calculate its differential characteristics, the implicit function theorem is used. The same theorem is used to reduce the approximation error to an acceptable level.

Features of the practical implementation feedback function method, including estimates of the rate of convergence to the exact solution are demonstrated for several specific classes of parametric optimization problems. Specifically, tasks searching for the global extremum of functions of many variables and the problem of multiple extremum (maximin-minimax) are considered. Optimization problems that arise when using multicriteria mathematical models are also considered. For each of these classes, there are demo examples.

The numerical solving of the system of high-temperature radiative gas dynamics (HTRGD) equations is a computationally laborious task, since the interaction of radiation with matter is nonlinear and non-local. The radiation absorption coefficients depend on temperature, and the temperature field is determined by both gas-dynamic processes and radiation transport. The method of splitting into physical processes is usually used to solve the HTRGD system, one of the blocks consists of a joint solving of the radiative transport equation and the energy balance equation of matter under known pressure and temperature fields. Usually difference schemes with orders of convergence no higher than the second are used to solve this block. Due to computer memory limitations it is necessary to use not too detailed grids to solve complex technical problems. This increases the requirements for the order of approximation of difference schemes. In this work, bicompact schemes of a high order of approximation for the algorithm for the joint solution of the radiative transport equation and the energy balance equation are implemented for the first time. The proposed method can be applied to solve a wide range of practical problems, as it has high accuracy and it is suitable for solving problems with coefficient discontinuities. The non-linearity of the problem and the use of an implicit scheme lead to an iterative process that may slowly converge. In this paper, we use a multiplicative HOLO algorithm named the quasi-diffusion method by V.Ya.Goldin. The key idea of HOLO algorithms is the joint solving of high order (HO) and low order (LO) equations. The high-order equation (HO) is the radiative transport equation solved in the energy multigroup approximation, the system of quasi-diffusion equations in the multigroup approximation (LO₁) is obtained by averaging HO equations over the angular variable. The next step is averaging over energy, resulting in an effective one-group system of quasi-diffusion equations (LO₂), which is solved jointly with the energy equation. The solutions obtained at each stage of the HOLO algorithm are closely related that ultimately leads to an acceleration of the convergence of the iterative process. Difference schemes constructed by the method of lines within one cell are proposed for each of the stages of the HOLO algorithm. The schemes have the fourth order of approximation in space and the third order of approximation in time. Schemes for the transport equation were developed by B.V. Rogov and his colleagues, the schemes for the LO₁ and LO₂ equations were developed by the authors. An analytical test is constructed to demonstrate the declared orders of convergence. Various options for setting boundary conditions are considered and their influence on the order of convergence in time and space is studied.

A compact finite difference scheme has been developed for modeling convection in a porous medium saturated with a fluid. We consider the problem for a rectangular domain with anisotropic permeability and thermal conductivity properties in terms of stream function and temperature deviation, taking into account Darcy's law. Boundary conditions of impenetrability and a linear distribution of temperature are set. This model is cosymmetric when certain conditions are imposed on the permeability and thermal conductivities. One parametric family of stationary convection regimes arises when mechanical equilibrium loses stability. A numerical method with a fourth-order finite difference approximation for spatial variables and a Runge – Kutta integrator for time has been developed. It has been proved that this scheme preserves cosymmetry. Numerical results for evaluating the critical Rayleigh number have been presented. We compare them with results obtained using a second-order finite-difference method. We show that critical Rayleigh numbers are repeated twice with very high accuracy, which proves cosymmetry preservation. Numerical evaluation of convective regimes and spectral properties are presented. The efficiency of the developed compact finite difference scheme on a nine-point stencil is assessed.

The article presents updated technologies for solving two classes of linear resource allocation problems with dynamically changing characteristics of situational management systems and awareness of experts (and/or trained robots). The search for solutions is carried out in an interactive mode of computational experiment using updatable knowledge systems about problems considered as constructive objects (in accordance with the methodology of formalization of knowledge about programmable problems created in the theory of S-symbols). The technologies are focused on implementation in the form of Internet services. The first class includes resource allocation problems solved by the method of targeted solution movement. The second is the problems of allocating a single resource in hierarchical systems, taking into account the priorities of expense items, which can be solved (depending on the specified mandatory and orienting requirements for the solution) either by the interval method of allocation (with input data and result represented by numerical segments), or by the targeted solution movement method. The problem statements are determined by requirements for solutions and specifications of their applicability, which are set by an expert based on the results of the portraits of the target and achieved situations analysis. Unlike well-known methods for solving resource allocation problems as linear programming problems, the method of targeted solution movement is insensitive to small data changes and allows to find feasible solutions when the constraint system is incompatible. In single-resource allocation technologies, the segmented representation of data and results allows a more adequate (compared to a point representation) reflection of the state of system resource space and increases the practical applicability of solutions. The technologies discussed in the article are programmatically implemented and used to solve the problems of resource basement for decisions, budget design taking into account the priorities of expense items, etc. The technology of allocating a single resource is implemented in the form of an existing online cost planning service. The methodological consistency of the technologies is confirmed by the results of comparison with known technologies for solving the problems under consideration.

This article proposes a numeric method of solution of quasilinear parabolic equations, based on the flux approximation, describes the implementation of the method on a rectangular grid and presents numerical results. Unlike methods used in common practice, this method uses an approximation of flows in non-dilated template. For each iteration of the Newton method it is possible to solve a linear problem using the method of upper relaxation (SOR). Compared with the methods of flux sweeping, the considered method has greater potential for use in modern parallel computing system.

In the paper we construct the adjoint problem for the explicit and implicit parabolic quazi-linear grid boundary-value problems with one spatial variable; the coefficients of the problems depend on the solution at the same time and earlier times. Dependence on the history of the solution is via the state vector; its evolution is described by the differential equation. Many models of diffusion mass transport are reduced to such boundary-value problems. Having solutions to the direct and adjoint problems, one can obtain the exact value of the gradient of a functional in the space of parameters the problem also depends on. We present solving algorithms, including the parallel one.

The article presents an approach to configuration of an artificial neural network architecture and a training set size. Configuration is based on parameter minimization with constraints specifying neural network model quality criteria. The algorithm of artificial neural network architecture and training set size configuration is applied to dynamic object artificial neural network approximation.
Series of computational experiments were performed. The method is applicable to construction of dynamic object models based on non-linear autocorrelation neural networks.

An approach to the decrease of norm of the correction in Newton’s methods of optimization, based on the Cholesky’s factorization is presented, which is based on the integration with the technique of the choice of leading element of algorithm of linear programming as a method of solving the system of equations. We investigate the issues of increasing of the numerical stability of the Cholesky’s decomposition and the Gauss’ method of exception.

We investigate machine learning methods with a certain kind of decision rule. In particular, inverse-distance method of interpolation, method of interpolation by radial basis functions, the method of multidimensional interpolation and approximation, based on the theory of random functions, the last method of interpolation is kriging. This paper shows a method of rapid retraining “model” when adding new data to the existing ones. The term “model” means interpolating or approximating function constructed from the training data. This approach reduces the computational complexity of constructing an updated “model” from $O(n^3)$ to $O(n^2)$. We also investigate the possibility of a rapid assessment of generalizing opportunities “model” on the training set using the method of cross-validation leave-one-out cross-validation, eliminating the major drawback of this approach — the necessity to build a new “model” for each element which is removed from the training set.