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In this paper we describe an algorithm of numerical solving of an inverse problem on a hyperbolic heat equation with additional second time derivative with a small parameter. The problem in this case is finding an initial distribution with given final distribution. This algorithm allows finding a solution to the problem for any admissible given precision. Algorithm allows evading difficulties analogous to the case of heat equation with inverted time. Furthermore, it allows finding an optimal grid size by learning on a relatively big grid size and small amount of iterations of a gradient method and later extrapolates to the required grid size using Richardson’s method. This algorithm allows finding an adequate estimate of Lipschitz constant for the gradient of the target functional. Finally, this algorithm may easily be applied to the problems with similar structure, for example in solving equations for plasma, social processes and various biological problems. The theoretical novelty of the paper consists in the developing of an optimal procedure of finding of the required grid size using Richardson extrapolations for optimization problems with inexact gradient in ill-posed problems.

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.

Conjugate gradient algorithms represent an important class of unconstrained optimization algorithms with strong local and global convergence properties and simple memory requirements. These algorithms have advantages that place them between the steep regression method and Newton’s algorithm because they require calculating the first derivatives only and do not require calculating and storing the second derivatives that Newton’s algorithm needs. They are also faster than the steep descent algorithm, meaning that they have overcome the slow convergence of this algorithm, and it does not need to calculate the Hessian matrix or any of its approximations, so it is widely used in optimization applications. This study proposes a novel method for image restoration by fusing the convex combination method with the hybrid (CG) method to create a hybrid three-term (CG) algorithm. Combining the features of both the Fletcher and Revees (FR) conjugate parameter and the hybrid Fletcher and Revees (FR), we get the search direction conjugate parameter. The search direction is the result of concatenating the gradient direction, the previous search direction, and the gradient from the previous iteration. We have shown that the new algorithm possesses the properties of global convergence and descent when using an inexact search line, relying on the standard Wolfe conditions, and using some assumptions. To guarantee the effectiveness of the suggested algorithm and processing image restoration problems. The numerical results of the new algorithm show high efficiency and accuracy in image restoration and speed of convergence when used in image restoration problems compared to Fletcher and Revees (FR) and three-term Fletcher and Revees (TTFR).

The need to model high-speed flows of compressible media with shock waves in the presence of dense curtains or layers of particles arises when studying various processes, such as the dispersion of particles from a layer behind a shock wave or propagation of combustion waves in heterogeneous explosives. These directions have been successfully developed over the past few decades, but the corresponding mathematical models and computational algorithms continue to be actively improved. The mechanisms of wave processes in two-phase media differ in different models, so it is important to continue researching and improving these models.

The paper is devoted to the numerical study of the propagation of disturbances inside a sand bed under the action of successive impacts of a normally incident air shock wave. The setting of the problem follows the experiments of A. T.Akhmetov with co-authors. The aim of this study is to investigate the possible reasons for signal amplification on the pressure sensor within the bed, as observed under some conditions in experiments. The mathematical model is based on a one-dimensional system of Baer –Nunziato equations for describing dense flows of two-phase media taking into account intergranular stresses in the particle phase. The computational algorithm is based on the Godunov method for the Baer – Nunziato equations.

The paper describes the dynamics of waves inside and outside a particle bed after applying first and second pressure pulses to it. The main components of the flow within the bed are filtration waves in the gas phase and compaction waves in the solid phase. The compaction wave, generated by the first pulse and reflected from the walls of the shock tube, interacts with the filtration wave caused by the second pulse. As a result, the signal measured by the pressure sensor inside the bed has a sharp peak, explaining the new effect observed in experiments.

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.

Searching optimal trajectory of motion is a complex problem that is investigated in many research studies. Most of the studies investigate methods that are applicable to such a problem in general, regardless of the model of the object. With such general approach, only numerical solution can be found. However, in some cases it is possible to find an optimal trajectory in a closed form. Current article considers a time optimal problem with state limitations for a wheeled mobile differential robot that moves on a horizontal plane. The mathematical model of motion is kinematic. The state constraints correspond to the obstacles on the plane defined as circles that need to be avoided during motion. The independent control inputs are the wheel speeds that are limited in absolute value. Such model is commonly used in problems where the transients are considered insignificant, for example, when controlling tracked or wheeled devices that move slowly, prioritizing traction power over speed. In the article it is shown that the optimal trajectory from the starting point to the finishing point in such kinematic approach is a sequence of straight segments of tangents to the obstacles and arcs of the circles that limit the obstacles. The geometrically shortest path between the start and the finish is also a sequence of straight lines and arcs, therefore the time-optimal trajectory corresponds to one of the local minima when searching for the shortest path. The article proposes a method of search for the time-optimal trajectory based on building a graph of possible trajectories, where the edges are the possible segments of the tajectory, and the vertices are the connections between them. The optimal path is sought using Dijkstra’s algorithm. The theoretical foundation of the method is given, and the results of computer investigation of the algorithm are provided.

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 paper is devoted to the problem of edge criticality identification and ranking in complex networks, which is a part of a modern research direction in the novel network science. The diffusion importance belongs to the set of acknowledged methods that help to identify the significant connections in the graph that are critical to retaining structural integrity. In the present work, we develop the Iterative Diffusion Importance algorithm that is based on the re-estimation of critical topological features at each step of the graph deconstruction. The Iterative Diffusion Importance has been compared with methods such as diffusion importance and degree product, which are two very well-known benchmark algorithms. As for benchmark networks, we tested the Iterative Diffusion Importance on three standard networks, such as Zachary’s Karate Club, the American Football Network, and the Dolphins Network, which are often used for algorithm efficiency evaluation and are different in size and density. Also, we proposed a new benchmark network representing the airplane communication between Japan and the US. The numerical experiment on finding the ranking of critical edges and the following network decomposition demonstrated that the proposed Iterative Diffusion Importance exceeds the conventional diffusion importance by the efficiency for 2–35% depending on the network complexity, the number of nodes, and the number of edges. The only drawback of the Iterative Diffusion Importance is an increase in computation complexity and hencely in the runtime, but this drawback can be easily compensated for by the preliminary planning of the network deconstruction or protection and by reducing the re-evaluation frequency of the iterative process.

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.

Statement of a problem of management of movement of a body in a viscous liquid is given. Movement bodies it is induced by moving of internal material points. On a basis the numerical decision of the equations of movement of a body and the hydrodynamic equations approximating dependencies for viscous forces are received. With application approximations the problem of optimum control of body movement dares on the set trajectory with application of hybrid genetic algorithm. Possibility of the directed movement of a body under action is established back and forth motion of an internal point. Optimum control movement direction it is carried out by motion of other internal point on circular trajectory with variable speed.