All issues

Small practical value of many numerical methods for solving single-ended systems of linear equations with ill-conditioned matrices due to the fact that these methods in the practice behave quite differently than in the case of precise calculations. Historically, sustainability is not enough attention was given, unlike in numerical algebra ‘medium-sized’, and emphasis is given to solving the problems of maximal order in data capabilities of the computer, including the expense of some loss of accuracy. Therefore, the main objects of study is the most appropriate storage of information contained in the sparse matrix; maintaining the highest degree of rarefaction at all stages of the computational process. Thus, the development of efficient numerical methods for solving unstable systems refers to the actual problems of computational mathematics.

In this paper, the approach to the construction of numerically stable direct multiplier methods for solving systems of linear equations, taking into account sparseness of matrices, presented in packaged form. The advantage of the approach consists in minimization of filling the main lines of the multipliers without compromising accuracy of the results and changes in the position of the next processed row of the matrix are made that allows you to use static data storage formats. The storage format of sparse matrices has been studied and the advantage of this format consists in possibility of parallel execution any matrix operations without unboxing, which significantly reduces the execution time and memory footprint.

Direct multiplier methods for solving systems of linear equations are best suited for solving problems of large size on a computer — sparse matrix systems allow you to get multipliers, the main row of which is also sparse, and the operation of multiplication of a vector-row of the multiplier according to the complexity proportional to the number of nonzero elements of this multiplier.

As a direct continuation of this work is proposed in the basis for constructing a direct multiplier algorithm of linear programming to put a modification of the direct multiplier algorithm for solving systems of linear equations based on integration of technique of linear programming for methods to select the host item. Direct multiplicative methods of linear programming are best suited for the construction of a direct multiplicative algorithm set the direction of descent Newton methods in unconstrained optimization by integrating one of the existing design techniques significantly positive definite matrix of the second derivatives.

Multi-label classification models arise in various areas of life, which is explained by an increasing amount of information that requires prompt analysis. One of the mathematical methods for solving this problem is a plug-in approach, at the first stage of which, for each class, a certain ranking function is built, ordering all objects in some way, and at the second stage, the optimal thresholds are selected, the objects on one side of which are assigned to the current class, and on the other — to the other. Thresholds are chosen to maximize the target quality measure. The algorithms which properties are investigated in this article are devoted to the second stage of the plug-in approach which is the choice of the optimal threshold vector. This step becomes non-trivial if the $F$-measure of average precision and recall is used as the target quality assessment since it does not allow independent threshold optimization in each class. In problems of extreme multi-label classification, the number of classes can reach hundreds of thousands, so the original optimization problem is reduced to the problem of searching a fixed point of a specially introduced transformation $\boldsymbol V$, defined on a unit square on the plane of average precision $P$ and recall $R$. Using this transformation, two algorithms are proposed for optimization: the $F$-measure linearization method and the method of $\boldsymbol V$ domain analysis. The properties of algorithms are studied when applied to multi-label classification data sets of various sizes and origin, in particular, the dependence of the error on the number of classes, on the $F$-measure parameter, and on the internal parameters of methods under study. The peculiarity of both algorithms work when used for problems with the domain of $\boldsymbol V$, containing large linear boundaries, was found. In case when the optimal point is located in the vicinity of these boundaries, the errors of both methods do not decrease with an increase in the number of classes. In this case, the linearization method quite accurately determines the argument of the optimal point, while the method of $\boldsymbol V$ domain analysis — the polar radius.

One- and two-dimensional hydrodynamic models of turbulent transfer within the atmospheric surface layer under neutral thermal stratification are considered. Both models are based on the solution of system of the timeaveraged equations of Navier – Stokes and continuity using a 1.5-order closure scheme as well as equations for turbulent kinetic energy and the rate of its dissipation. The influence of the upper and lower boundary conditions on vertical profiles of wind speed and turbulence parameters within the atmospheric surface layer was derived using an one-dimensional model usually applied in case of an uniform ground surface. The boundary conditions in the model were prescribed in such way that the vertical wind and turbulence patterns were well agreed with widely used logarithmic vertical profile of wind speed, linear dependence of turbulent exchange coefficient on height above ground surface level and constancy of turbulent kinetic energy within the atmospheric surface layer under neutral atmospheric conditions. On the basis of the classical one-dimensional model it is possible to obtain a number of relationships which link the vertical wind speed gradient, turbulent kinetic energy and the rate of its dissipation. Each of these relationships can be used as a boundary condition in our hydrodynamic model. The boundary conditions for the wind speed and the rate of dissipation of turbulent kinetic energy were selected as parameters to provide the smallest deviations of model calculations from classical distributions of wind and turbulence parameters. The corresponding upper and lower boundary conditions were used to define the initial and boundary value problem in the two-dimensional hydrodynamic model allowing to consider complex topography and horizontal vegetation heterogeneity. The two-dimensional model with selected optimal boundary conditions was used to describe the spatial pattern of turbulent air flow when it interacted with the forest edge. The dynamics of the air flow establishment depending on the distance from the forest edge was analyzed. For all considered initial and boundary value problems the unconditionally stable implicit finite-difference schemes of their numerical solution were developed and implemented.

An essential class of problems describes physical processes occurring in non-convex domains containing a corner greater than 180 degrees on the boundary. The solution in a neighborhood of a corner is singular and its finding using classical approaches entails a loss of accuracy. In the paper, we consider stationary, linearized by Picard’s iterations, Navier – Stokes equations governing the flow of a incompressible viscous fluid in the convection form in $L$-shaped domain. An $R_\nu$-generalized solution of the problem in special sets of weighted spaces is defined. A special finite element method to find an approximate $R_\nu$-generalized solution is constructed. Firstly, functions of the finite element spaces satisfy the law of conservation of mass in the strong sense, i.e. at the grid nodes. For this purpose, Scott – Vogelius element pair is used. The fulfillment of the condition of mass conservation leads to the finding more accurate, from a physical point of view, solution. Secondly, basis functions of the finite element spaces are supplemented by weight functions. The degree of the weight function, as well as the parameter $\nu$ in the definition of an $R_\nu$-generalized solution, and a radius of a neighborhood of the singularity point are free parameters of the method. A specially selected combination of them leads to an increase almost twice in the order of convergence rate of an approximate solution to the exact one in relation to the classical approaches. The convergence rate reaches the first order by the grid step in the norms of Sobolev weight spaces. Thus, numerically shown that the convergence rate does not depend on the corner value.

A numerical method is proposed that approximates the equations of the dynamics of a weakly compressible viscous flow in the presence of a polymer component of the flow. The behavior of the flow under the influence of a static external periodic force in a periodic square cell is investigated. The methodology is based on a hybrid approach. The hydrodynamics of the flow is described by a system of Navier – Stokes equations and is numerically approximated by the linearized Godunov method. The polymer field is described by a system of equations for the vector of stretching of polymer molecules $\bf R$, which is numerically approximated by the Kurganov – Tedmor method. The choice of model relationships in the development of a numerical methodology and the selection of modeling parameters made it possible to qualitatively model and study the regime of elastic turbulence at low Reynolds $Re \sim 10^{-1}$. The polymer solution flow dynamics equations differ from the Newtonian fluid dynamics equations by the presence on the right side of the terms describing the forces acting on the polymer component part. The proportionality coefficient $A$ for these terms characterizes the backward influence degree of the polymers number on the flow. The article examines in detail how the flow and its characteristics change depending on the given coefficient. It is shown that with its growth, the flow becomes more chaotic. The flow energy spectra and the spectra of the polymers stretching field are constructed for different values of $A$. In the spectra, an inertial sub-range of the energy cascade is traced for the flow velocity with an indicator $k \sim −4$, for the cascade of polymer molecules stretches with an indicator $−1.6$.

This article studies a method of constructing a predictive neural network model of a time series based on determining the composition of input variables, constructing a training sample and training itself using the back propagation method. Traditional methods of constructing predictive models of the time series are: the autoregressive model, the moving average model or the autoregressive model — the moving average allows us to approximate the time series by a linear dependence of the current value of the output variable on a number of its previous values. Such a limitation as linearity of dependence leads to significant errors in forecasting.

Mining Technologies using neural network modeling make it possible to approximate the time series by a nonlinear dependence. Moreover, the process of constructing of a neural network model (determining the composition of input variables, the number of layers and the number of neurons in the layers, choosing the activation functions of neurons, determining the optimal values of the neuron link weights) allows us to obtain a predictive model in the form of an analytical nonlinear dependence.

The determination of the composition of input variables of neural network models is one of the key points in the construction of neural network models in various application areas that affect its adequacy. The composition of the input variables is traditionally selected from some physical considerations or by the selection method. In this work it is proposed to use the behavior of the autocorrelation and private autocorrelation functions for the task of determining the composition of the input variables of the predictive neural network model of the time series.

In this work is proposed a method for determining the composition of input variables of neural network models for stationary and non-stationary time series, based on the construction and analysis of autocorrelation functions. Based on the proposed method in the Python programming environment are developed an algorithm and a program, determining the composition of the input variables of the predictive neural network model — the perceptron, as well as building the model itself. The proposed method was experimentally tested using the example of constructing a predictive neural network model of a time series that reflects energy consumption in different regions of the United States, openly published by PJM Interconnection LLC (PJM) — a regional network organization in the United States. This time series is non-stationary and is characterized by the presence of both a trend and seasonality. Prediction of the next values of the time series based on previous values and the constructed neural network model showed high approximation accuracy, which proves the effectiveness of the proposed method.

The propagation of stable coherent entities of an electromagnetic field in nonlinear media with parameters varying in space can be described in the framework of iterations of nonlinear integral transformations. It is shown that for a set of geometries relevant to typical problems of nonlinear optics, numerical modeling by reducing to dynamical systems with discrete time and continuous spatial variables to iterates of local nonlinear Feigenbaum and Ikeda mappings and nonlocal diffusion-dispersion linear integral transforms is equivalent to partial differential equations of the Ginzburg–Landau type in a fairly wide range of parameters. Such nonlocal mappings, which are the products of matrix operators in the numerical implementation, turn out to be stable numerical- difference schemes, provide fast convergence and an adequate approximation of solutions. The realism of this approach allows one to take into account the effect of noise on nonlinear dynamics by superimposing a spatial noise specified in the form of a multimode random process at each iteration and selecting the stable wave configurations. The nonlinear wave formations described by this method include optical phase singularities, spatial solitons, and turbulent states with fast decay of correlations. The particular interest is in the periodic configurations of the electromagnetic field obtained by this numerical method that arise as a result of phase synchronization, such as optical lattices and self-organized vortex clusters.

A model of combustion of premixed mixture of gases with one global chemical reaction is considered, the model includes equations of the second order for temperature of mixture and concentrations of fuel and oxidizer, and the right-hand sides of these equations contain the reaction rate function. This function depends on five unknown parameters of the global reaction and serves as approximation to multistep reaction mechanism. The model is reduced, after replacement of variables, to one equation of the second order for temperature of mixture that transforms to a first-order equation for temperature derivative depending on temperature that contains a parameter of flame propagation velocity. Thus, for computing the parameter of burning velocity, one has to solve Dirichlet problem for first-order equation, and after that a model dependence of burning velocity on mixture equivalence ratio at specified reaction rate parameters will be obtained. Given the experimental data of dependence of burning velocity on mixture equivalence ratio, the problem of optimal selection of reaction rate parameters is stated, based on minimization of the mean square deviation of model values of burning velocity on experimental ones. The aim of our study is analysis of uniqueness of this problem solution. To this end, we apply computational experiment during which the problem of global search of optima is solved using multistart of gradient descent. The computational experiment clarifies that the inverse problem in this statement is underdetermined, and every time, when running gradient descent from a selected starting point, it converges to a new limit point. The structure of the set of limit points in the five-dimensional space is analyzed, and it is shown that this set can be described with three linear equations. Therefore, it might be incorrect to tabulate all five parameters of reaction rate based on just one match criterion between model and experimental data of flame propagation velocity. The conclusion of our study is that in order to tabulate reaction rate parameters correctly, it is necessary to specify the values of two of them, based on additional optimality criteria.

The work is devoted to the problem of creating a model with stationary parameters using historical data under conditions of unknown disturbances. The case is considered when a representative sample of object states can be formed using historical data accumulated only over a significant period of time. It is assumed that unknown disturbances can act in a wide frequency range and may have low-frequency and trend components. In such a situation, including data from different time periods in the sample can lead to inconsistencies and greatly reduce the accuracy of the model. The paper provides an overview of approaches and methods for data harmonization. In this case, the main attention is paid to data sampling. An assessment is made of the applicability of various data sampling options as a tool for reducing the level of uncertainty. We propose a method for identifying a self-leveling object model using data accumulated over a significant period of time under conditions of unknown disturbances with a wide frequency range. The method is focused on creating a model with stationary parameters that does not require periodic reconfiguration to new conditions. The method is based on the combined use of sampling and presentation of data from individual periods of time in the form of increments relative to the initial point in time for the period. This makes it possible to reduce the number of parameters that characterize unknown disturbances with a minimum of assumptions that limit the application of the method. As a result, the dimensionality of the search problem is reduced and the computational costs associated with setting up the model are minimized. It is possible to configure both linear and, in some cases, nonlinear models. The method was used to develop a model of closed cooling of steel on a unit for continuous hot-dip galvanizing of steel strip. The model can be used for predictive control of thermal processes and for selecting strip speed. It is shown that the method makes it possible to develop a model of thermal processes from a closed cooling section under conditions of unknown disturbances, including low-frequency components.