scholarly journals SYNTHESIS TRENDS OF FORECASTING USING INDUCTIVE MODELING METHODS

2021 ◽  
Vol 3 (65) ◽  
pp. 108-112
Author(s):  
E. Skakalina
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Numerical Solutions ◽  
Group Method ◽  
Software Environment ◽  
Optimization Analysis ◽  
Modern Development ◽  
Parametric Data ◽  
Parametric Functions ◽  
Direct Problems

Modern development of computer technology and the possibility of implementing calculations in parallel allow to solve increasingly large-scale problems of numerical modeling. The development of multiprocessor computing and parallel computing makes it important to solve problems of optimization analysis. The optimization analysis is based on the mass solution of inverse problems when the defining parameters of the considered class of problems change in certain ranges. Thus, calculations of not only direct problems where it is necessary to model the phenomenon at the known initial data, but also calculations of inverse problems where it is necessary to define on what defining parameters there is this or that phenomenon become more and more demanded. This formulation requires multiple solutions of direct problems and solving the problem of optimization analysis and construction of predictive trends. Sets of multidimensional parametric data in the paper are considered as numerical solutions of the optimization problem. The construction of predictive trends is implemented on the basis of the group method of data handling as a direction of induction modeling. The methodology of visualization of results of calculation of parametric functions is realized. The scheme of Data Mining with application of methods of visualization by means of the Matlab software environment is described

An object-oriented optimization framework for large-scale inverse problems

2021 ◽  
pp. 104790
Author(s):  
Ettore Biondi ◽  
Guillaume Barnier ◽  
Robert G. Clapp ◽  
Francesco Picetti ◽  
Stuart Farris
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Object Oriented ◽  
Optimization Framework

hIPPYlib

2021 ◽  
Vol 47 (2) ◽  
pp. 1-34
Author(s):  
Umberto Villa ◽  
Noemi Petra ◽  
Omar Ghattas
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Low Rank ◽  
Scalable Algorithms ◽  
Low Rank Approximation ◽  
Dimensional Parameter ◽  
Infinite Dimensional ◽  
Bayesian Inverse Problems ◽  
Rank Approximation ◽  
Extensible Software

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.

Cooperative Coevolution with Formula-Based Variable Grouping for Large-Scale Global Optimization

2018 ◽  
Vol 26 (4) ◽  
pp. 569-596 ◽  
Author(s):  
Yuping Wang ◽  
Haiyan Liu ◽  
Fei Wei ◽  
Tingting Zong ◽  
Xiaodong Li
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Real World ◽  
Large Scale ◽  
Divide And Conquer ◽  
Group Method ◽  
Objective Functions ◽  
Elementary Functions ◽  
Cooperative Coevolution ◽  
Solution Quality

For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered an effective strategy to decompose the problem into smaller subproblems, each of which can then be solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real-world problems are white-box problems, that is, the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can then be used to design an effective variable group method. In this article, a formula-based grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations “[Formula: see text]”, “[Formula: see text]”, “[Formula: see text]”, “[Formula: see text]” and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in nonseparable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being interdependent. FBG can easily be applied to any white-box problem and can be integrated into a cooperative coevolution framework. Based on FBG, a novel cooperative coevolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this article for decomposing a large-scale white-box problem into several smaller subproblems and optimizing them respectively. To further enhance the efficiency of CCF, a new local search scheme is designed to improve the solution quality. To verify the efficiency of CCF, experiments are conducted on the standard LSGO benchmark suites of CEC'2008, CEC'2010, CEC'2013, and a real-world problem. Our results suggest that the performance of CCF is very competitive when compared with those of the state-of-the-art LSGO algorithms.

Strategic Guidelines of the State Library Policy

2021 ◽  
Vol 70 (2) ◽  
pp. 119-129
Author(s):  
Irina A. Trushina
Keyword(s):  
Russian Federation ◽  
Professional Education ◽  
Large Scale ◽  
Research Work ◽  
Professional Staff ◽  
The State ◽  
Educational Work ◽  
Modern Development ◽  
The Russian Federation ◽  
State Library

The Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia was held on November 11—12, 2020 in the online format. The event was organized by the Ministry of Culture of the Russian Federation, the Russian National Library and the Russian State Library. The main goal of the meeting is to ensure participation of the heads of federal and central regional libraries in the formation and implementation of the state library policy. The topic of the 2020 Meeting is “The Library Profession and Scientific and Educational Work of Libraries”. The scientific content of the meeting was basically determined by the “Strategy for the development of librarianship in the Russian Federation for the period up to 2030”, the draft development of which has been already completed as a whole, but requires the deployment of large-scale research work to form the unified system for monitoring the activities of libraries in the country.The meeting focused on the following issues: organization of scientific research work in libraries; training of professional staff; modernization of librarianship and the role of information technologies in the modern development of libraries and digitalization. The relevance of these topics was proved in the discourse on the development of higher and further professional education in the library sector, improvement of availability of information in the modern conditions. During the sessions, there were summed up the results of the 7th All-Russian competition “Library Analytics” among the central libraries of the subjects of the Russian Federation, the 8th All-Russian competition “The Best Professional Book of the Year” and the 7th All-Russian library review competition for the best electronic publication on culture and art.

Cloud in Science

2014 ◽  
pp. 165-191
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Computing ◽  
Scientific Discovery ◽  
Software Environment ◽  
Access To Resources ◽  
On Demand ◽  
Service Models ◽  
New Models ◽  
The Impact

Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources as well as the ability to control the software environment. Scientific computing researchers and resource providers servicing these users are considering the impact of new models and technologies. SaaS solutions like Globus Online and IaaS solutions such as Nimbus Infrastructure and OpenNebula accelerate the discovery of science by helping scientists to conduct advanced and large-scale science. This chapter describes how cloud is helping researchers to accelerate scientific discovery by transforming manual and difficult tasks into the cloud.

Large-Scale Inverse Problems in Imaging

2011 ◽  
pp. 43-86 ◽  
Author(s):  
Julianne Chung ◽  
Sarah Knepper ◽  
James G. Nagy
Keyword(s):  
Inverse Problems ◽  
Large Scale
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