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The problem of restoration of an element f of Euclidean functional space  L²(X) based on the results of measurements of a finite set of its linear functionals, distorted by (random) error is solved. A priori data aren't assumed. Family of linear subspaces of the maximum (effective) dimension for which the projections of element f to them allow estimates with a given accuracy, is received. The effective rank ρ(δ) of the estimation problem is defined as the function equal to the maximum dimension of an orthogonal component Pf of the element f which can be estimated with a error, which is not surpassed the value δ. The example of restoration of a spectrum of radiation based on a finite set of experimental data is given.

We consider a model of stacked long Josephson junctions (LJJ), which consists of alternating superconducting and dielectric layers. The model takes into account the inductive and capacitive coupling between the neighbor junctions. The model is described by a system of nonlinear partial differential equations with respect to the phase differences and the voltage of LJJ, with appropriate initial and boundary conditions. The numerical solution of this system of equations is based on the use of standard three-point finite-difference formulae for discrete approximations in the space coordinate, and the applying the four-step Runge-Kutta method for solving the Cauchy problem obtained. Designed parallel algorithm is implemented by means of the MPI technology (Message Passing Interface). In the paper, the mathematical formulation of the problem is given, numerical scheme and a method of calculation of the current-voltage characteristics of the LJJ system are described. Two variants of parallel implementation are presented. The influence of inductive and capacitive coupling between junctions on the structure of the current-voltage characteristics is demonstrated. The results of methodical calculations with various parameters of length and number of Josephson junctions in the LJJ stack depending on the number of parallel computing nodes, are presented. The calculations have been performed on multiprocessor clusters HybriLIT and CICC of Multi-Functional Information and Computing Complex (Laboratory of Information Technologies, Joint Institute for Nuclear Research, Dubna). The numerical results are discussed from the viewpoint of the effectiveness of presented approaches of the LJJ system numerical simulation in parallel. It has been shown that one of parallel algorithms provides the 9 times speedup of calculations.

A new statement of Integral Geometry problem where the image of function in each point is taken as an integral with respect to measure which depends on the point is suggested. Such Measure System is named Measure Induction. It is shown that an inversion formula exists for class of measures having a unit atom in corresponding
point and limited on whole space. Previously obtained results for average on systems of measurement dissections and for weight average on graphs are generalized.

The problem of online convex optimization naturally occurs in cases when there is an update of statistical information. The mirror descent method is well known for non-smooth optimization problems. Mirror descent is an extension of the subgradient method for solving non-smooth convex optimization problems in the case of a non-Euclidean distance. This paper is devoted to a stochastic variant of recently proposed Mirror Descent methods for convex online optimization problems with convex Lipschitz (generally, non-smooth) functional constraints. This means that we can still use the value of the functional constraint, but instead of (sub)gradient of the objective functional and the functional constraint, we use their stochastic (sub)gradients. More precisely, assume that on a closed subset of $n$-dimensional vector space, $N$ convex Lipschitz non-smooth functionals are given. The problem is to minimize the arithmetic mean of these functionals with a convex Lipschitz constraint. Two methods are proposed, for solving this problem, using stochastic (sub)gradients: adaptive method (does not require knowledge of Lipschitz constant neither for the objective functional, nor for the functional of constraint) and non-adaptivemethod (requires knowledge of Lipschitz constant for the objective functional and the functional of constraint). Note that it is allowed to calculate the stochastic (sub)gradient of each functional only once. In the case of non-negative regret, we find that the number of non-productive steps is $O$($N$), which indicates the optimality of the proposed methods. We consider an arbitrary proximal structure, which is essential for decisionmaking problems. The results of numerical experiments are presented, allowing to compare the work of adaptive and non-adaptive methods for some examples. It is shown that the adaptive method can significantly improve the number of the found solutions.

Simulation of turbulent compressible gas flows using turbulence models $k-\varepsilon$ standard (KES), $k-\varepsilon$ FlowVision (KEFV) and SST $k-\omega$ is discussed in the given article. A new version of turbulence model KEFV is presented. The results of its testing are shown. Numerical investigation of the discharge of an over-expanded jet from a conic nozzle into unlimited space is performed. The results are compared against experimental data. The dependence of the results on computational mesh is demonstrated. The dependence of the results on turbulence specified at the nozzle inlet is demonstrated. The conclusion is drawn about necessity to allow for compressibility in two-parametric turbulence models. The simple method proposed by Wilcox in 1994 suits well for this purpose. As a result, the range of applicability of the three aforementioned two-parametric turbulence models is essentially extended. Particular values of the constants responsible for the account of compressibility in the Wilcox approach are proposed. It is recommended to specify these values in simulations of compressible flows with use of models KES, KEFV, and SST.

In addition, the question how to obtain correct characteristics of supersonic turbulent flows using two-parametric turbulence models is considered. The calculations on different grids have shown that specifying a laminar flow at the inlet to the nozzle and wall functions at its surfaces, one obtains the laminar core of the flow up to the fifth Mach disk. In order to obtain correct flow characteristics, it is necessary either to specify two parameters characterizing turbulence of the inflowing gas, or to set a “starting” turbulence in a limited volume enveloping the region of presumable laminar-turbulent transition next to the exit from the nozzle. The latter possibility is implemented in model KEFV.

A direct algorithm for solving a linear programming problem (LP), given in canonical form, is considered. The algorithm consists of two successive stages, in which the following LP problems are solved by a direct method: a non-degenerate auxiliary problem at the first stage and some problem equivalent to the original one at the second. The construction of the auxiliary problem is based on a multiplicative version of the Gaussian exclusion method, in the very structure of which there are possibilities: identification of incompatibility and linear dependence of constraints; identification of variables whose optimal values are obviously zero; the actual exclusion of direct variables and the reduction of the dimension of the space in which the solution of the original problem is determined. In the process of actual exclusion of variables, the algorithm generates a sequence of multipliers, the main rows of which form a matrix of constraints of the auxiliary problem, and the possibility of minimizing the filling of the main rows of multipliers is inherent in the very structure of direct methods. At the same time, there is no need to transfer information (basis, plan and optimal value of the objective function) to the second stage of the algorithm and apply one of the ways to eliminate looping to guarantee final convergence.

Two variants of the algorithm for solving the auxiliary problem in conjugate canonical form are presented. The first one is based on its solution by a direct algorithm in terms of the simplex method, and the second one is based on solving a problem dual to it by the simplex method. It is shown that both variants of the algorithm for the same initial data (inputs) generate the same sequence of points: the basic solution and the current dual solution of the vector of row estimates. Hence, it is concluded that the direct algorithm is an algorithm of the simplex method type. It is also shown that the comparison of numerical schemes leads to the conclusion that the direct algorithm allows to reduce, according to the cubic law, the number of arithmetic operations necessary to solve the auxiliary problem, compared with the simplex method. An estimate of the number of iterations is given.

Currently, journal papers contain numerous examples of the use of heavy-tailed distributions for applied research on various complex systems. Models of extreme data are usually limited to a small set of distribution shapes that in this field of applied research historically been used. It is possible to increase the composition of the set of probability distributions shapes through comparing the measures of the distribution shapes and choosing the most suitable implementations. The example of a beta distribution of the second kind shown that the lack of definability of the moments of heavy-tailed implementations of the beta family of distributions limits the applicability of the existing classical methods of moments for studying the distributions shapes when are characterized heavy tails. For this reason, the development of new methods for comparing distributions based on quantile shape measures free from the restrictions on the shape parameters remains relevant study the possibility of constructing a space of quantile measures of shapes for comparing distributions with heavy tails. The operation purpose consists in computer research of creation possibility of space of the quantile’s measures for the comparing of distributions property with heavy tails. On the basis of computer simulation there the distributions implementations in measures space of shapes were been shown. Mapping distributions in space only of the parametrical measures of shapes has shown that the imposition of regions for heavy tails distribution made impossible compare the shape of distributions belonging to different type in the space of quantile measures of skewness and kurtosis. It is well known that shape information measures such as entropy and entropy uncertainty interval contain additional information about the shape measure of heavy-tailed distributions. In this paper, a quantile entropy coefficient is proposed as an additional independent measure of shape, which is based on the ratio of entropy and quantile uncertainty intervals. Also estimates of quantile entropy coefficients are obtained for a number of well-known heavy-tailed distributions. The possibility of comparing the distributions shapes with realizations of the beta distribution of the second kind is illustrated by the example of the lognormal distribution and the Pareto distribution. Due to mapping the position of stable distributions in the three-dimensional space of quantile measures of shapes estimate made it possible the shape parameters to of the beta distribution of the second kind, for which shape is closest to the Lévy shape. From the paper material it follows that the display of distributions in the three-dimensional space of quantile measures of the forms of skewness, kurtosis and entropy coefficient significantly expands the possibility of comparing the forms for distributions with heavy tails.

A boundary value problem for partial differential equation with nonlocal boundary relations of special type is resolved by means of a slight modification of the separation of variables method. Ordinal differential operator of the second order subject to boundary conditions of the main problem is not self-adjoint. The system of eigenfunctions generated by the operator has no basis property in L₂[0,1] space. A special system of functions is proposed to expand the solution of the boundary value problem.

The conditions for the existence of Feynman integrals in a sense of analytic continuation of the exponential functionals with a fourth-power polynomial in the index are studied, their presentations by Gaussian integrals are constructed in the paper. It is shown that the Schrodinger-type equation in the infinite-dimensional space in the case of fourth-power polynomial potential has a solution which is described by the Feynman path integral in configuration space.

A set of implicit difference schemes on the five-pointwise stensil is under construction. The analysis of properties of difference schemes is carried out in a space of undetermined coefficients. The spaces were introduced for the first time by A. S. Kholodov. Usually for properties of difference schemes investigation the problem of the linear programming was constructed. The coefficient at the main term of a discrepancy was considered as the target function. The optimization task with inequalities type restrictions was considered for construction of the monotonic difference schemes. The limitation of such an approach becomes clear taking into account that approximation of the difference scheme is defined only on the classical (smooth) solutions of partial differential equations.

The functional which minimum will be found put in compliance to the difference scheme. The functional must be the linear on the difference schemes coefficients. It is possible that the functional depends on net function – the solution of a difference task or a grid projection of the differential problem solution. If the initial terms of the functional expansion in a Taylor series on grid parameters are equal to conditions of classical approximation, we will call that the functional will be the generalized condition of approximation. It is shown that such functionals exist. For the simple linear partial differential equation with constant coefficients construction of the functional is possible also for the generalized (non-smooth) solution of a differential problem.

Families of functionals both for smooth solutions of an initial differential problem and for the generalized solution are constructed. The new difference schemes based on the analysis of the functionals by linear programming methods are constructed. At the same time the research of couple of self-dual problems of the linear programming is used. The optimum monotonic difference scheme possessing the first order of approximation on the smooth solution of differential problem is found. The possibility of application of the new schemes for creation of hybrid difference methods of the raised approximation order on smooth solutions is discussed.

The example of numerical implementation of the simplest difference scheme with the generalized approximation is given.