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In the development of the previously obtained result on modeling the dynamics of vegetation cover, due to variations in the temperature background, a new scheme for the interval analysis of the dynamics of floristic images of formations is presented in the case when the parameter of the response rate of the model of the dynamics of each counting plant species is set by the interval of scatter of its possible values. The detailed description of the functional parameters of macromodels of biodiversity, desired in fundamental research, taking into account the essential reasons for the observed evolutionary processes, may turn out to be a problematic task. The use of more reliable interval estimates of the variability of functional parameters “bypasses” the problem of uncertainty in the primary assessment of the evolution of the phyto-resource potential of the developed controlled territories. The solutions obtained preserve not only a qualitative picture of the dynamics of species diversity, but also give a rigorous, within the framework of the initial assumptions, a quantitative assessment of the degree of presence of each plant species. The practical significance of two-sided estimation schemes based on the construction of equations for the upper and lower boundaries of the trajectories of the scatter of solutions depends on the conditions and measure of proportional correspondence of the intervals of scatter of the initial parameters with the intervals of scatter of solutions. For dynamic systems, the desired proportionality is not always ensured. The given examples demonstrate the acceptable accuracy of interval estimation of evolutionary processes. It is important to note that the constructions of the estimating equations generate vanishing intervals of scatter of solutions for quasi-constant temperature perturbations of the system. In other words, the trajectories of stationary temperature states of the vegetation cover are not roughened by the proposed interval estimation scheme. The rigor of the result of interval estimation of the species composition of the vegetation cover of formations can become a determining factor when choosing a method in the problems of analyzing the dynamics of species diversity and the plant potential of territorial systems of resource-ecological monitoring. The possibilities of the proposed approach are illustrated by geoinformation images of the computational analysis of the dynamics of the vegetation cover of the Yamal Peninsula and by the graphs of the retro-perspective analysis of the floristic variability of the formations of the landscapelithological group “Upper” based on the data of the summer temperature background of the Salehard weather station from 2010 to 1935. The developed indicators of floristic variability and the given graphs characterize the dynamics of species diversity, both on average and individually in the form of intervals of possible states for each species of plant.

The article discusses the problem of the influence of the research goals on the structure of the multivariate model of regression analysis (in particular, on the implementation of the procedure for reducing the dimension of the model). It is shown how bringing the specification of the multiple regression model in line with the research objectives affects the choice of modeling methods. Two schemes for constructing a model are compared: the first does not allow taking into account the typology of primary predictors and the nature of their influence on the performance characteristics, the second scheme implies a stage of preliminary division of the initial predictors into groups, in accordance with the objectives of the study. Using the example of solving the problem of analyzing the causes of burnout of creative workers, the importance of the stage of qualitative analysis and systematization of a priori selected factors is shown, which is implemented not by computing means, but by attracting the knowledge and experience of specialists in the studied subject area. The presented example of the implementation of the approach to determining the specification of the regression model combines formalized mathematical and statistical procedures and the preceding stage of the classification of primary factors. The presence of this stage makes it possible to explain the scheme of managing (corrective) actions (softening the leadership style and increasing approval lead to a decrease in the manifestations of anxiety and stress, which, in turn, reduces the severity of the emotional exhaustion of the team members). Preclassification also allows avoiding the combination in one main component of controlled and uncontrolled, regulatory and controlled feature factors, which could worsen the interpretability of the synthesized predictors. On the example of a specific problem, it is shown that the selection of factors-regressors is a process that requires an individual solution. In the case under consideration, the following were consistently used: systematization of features, correlation analysis, principal component analysis, regression analysis. The first three methods made it possible to significantly reduce the dimension of the problem, which did not affect the achievement of the goal for which this task was posed: significant measures of controlling influence on the team were shown. allowing to reduce the degree of emotional burnout of its participants.

We propose a family of Gauss –Newton methods for solving optimization problems and systems of nonlinear equations based on the ideas of using the upper estimate of the norm of the residual of the system of nonlinear equations and quadratic regularization. The paper presents a development of the «Three Squares Method» scheme with the addition of a momentum term to the update rule of the sought parameters in the problem to be solved. The resulting scheme has several remarkable properties. First, the paper algorithmically describes a whole parametric family of methods that minimize functionals of a special kind: compositions of the residual of a nonlinear equation and an unimodal functional. Such a functional, entirely consistent with the «gray box» paradigm in the problem description, combines a large number of solvable problems related to applications in machine learning, with the regression problems. Secondly, the obtained family of methods is described as a generalization of several forms of the Levenberg –Marquardt algorithm, allowing implementation in non-Euclidean spaces as well. The algorithm describing the parametric family of Gauss –Newton methods uses an iterative procedure that performs an inexact parametrized proximal mapping and shift using a momentum term. The paper contains a detailed analysis of the efficiency of the proposed family of Gauss – Newton methods; the derived estimates take into account the number of external iterations of the algorithm for solving the main problem, the accuracy and computational complexity of the local model representation and oracle computation. Sublinear and linear convergence conditions based on the Polak – Lojasiewicz inequality are derived for the family of methods. In both observed convergence regimes, the Lipschitz property of the residual of the nonlinear system of equations is locally assumed. In addition to the theoretical analysis of the scheme, the paper studies the issues of its practical implementation. In particular, in the experiments conducted for the suboptimal step, the schemes of effective calculation of the approximation of the best step are given, which makes it possible to improve the convergence of the method in practice in comparison with the original «Three Square Method». The proposed scheme combines several existing and frequently used in practice modifications of the Gauss –Newton method, in addition, the paper proposes a monotone momentum modification of the family of developed methods, which does not slow down the search for a solution in the worst case and demonstrates in practice an improvement in the convergence of the method.

The paper discusses the design and verification stages in the development of complex numerical algorithms to create direct computational experiments in fluid mechanics. The modeling of physical fields and nonstationary processes of continuum mechanics, it is desirable to rely on strict rules of construction the numerical objects and related computational algorithms. Synthesis of adaptive the numerical objects and effective arithmetic- logic operations can serve to optimize the whole computing tasks, provided strict following and compliance with the original of the laws of fluid mechanics. The possibility of using ternary logic enables to resolve some contradictions of functional and declarative programming in the implementation of purely applied problems of mechanics. Similar design decisions lead to new numerical schemes tensor mathematics to help optimize effectiveness and validate correctness the simulation results. The most important consequence is the possibility of using interactive graphical techniques for the visualization of intermediate results of modeling, as well as managed to influence the course of computing experiment under the supervision of engineers aerohydrodynamics– researchers.

Course “Natural models of parallel computing”, given for senior students of the Faculty of Computational Mathematics and Cybernetics, Moscow State University, is devoted to the issues of supercomputer implementation of natural computational models and is, in fact, an introduction to the theory of natural computing, a relatively new branch of science, formed at the intersection of mathematics, computer science and natural sciences (especially biology). Topics of the natural computing include both already classic subjects such as cellular automata, and relatively new, introduced in the last 10–20 years, such as swarm intelligence. Despite its biological origin, all these models are widely applied in the fields related to computer data processing. Research in the field of natural computing is closely related to issues and technology of parallel computing. Presentation of theoretical material of the course is accompanied by a consideration of the possible schemes for parallel computing, in the practical part of the course it is supposed to perform by the students a software implementation using MPI technology and numerical experiments to investigate the effectiveness of the chosen schemes of parallel computing.