Vladimir Tsurkov's Books

Hierarchical Optimization and Mathematical Physics

By: Vladimir Tsurkov

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Hierarchical Optimization and Mathematical Physics

By: Vladimir Tsurkov

This book should be considered as an introduction to a special dass of hierarchical systems of optimal control, where subsystems are described by partial differential equations of various types. Optimization is carried out by means of a two-level scheme, where the center optimizes coordination for the upper level and subsystems find the optimal solutions for independent local problems. The main algorithm is a method of iterative aggregation. The coordinator solves the problern with macrovariables, whose number is less than the number of initial variables. This problern is often very simple. On the lower level, we have the usual optimal control problems of math ematical physics, which are far simpler than the initial statements. Thus, the decomposition (or reduction to problems ofless dimensions) is obtained. The algorithm constructs a sequence of so-called disaggregated solutions that are feasible for the main problern and converge to its optimal solutionunder certain assumptions ( e.g., under strict convexity of the input functions). Thus, we bridge the gap between two disciplines: optimization theory of large-scale systems and mathematical physics. The first motivation was a special model of branch planning, where the final product obeys a preset assortment relation. The ratio coefficient is maximized. Constraints are given in the form of linear inequalities with block diagonal structure of the part of a matrix that corresponds to subsystems. The central coordinator assem bles the final production from the components produced by the subsystems.

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Page Count

320

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Springer Science & Business Media

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ISBN 10

1461546672

ISBN 13

9781461546672

Large-scale Optimization

Problems and Methods

By: Vladimir Tsurkov

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Large-scale Optimization

Problems and Methods

By: Vladimir Tsurkov

Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms. Audience: This book is addressed to specialists in operations research, optimization, and optimal control.

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322

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Springer Science & Business Media

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ISBN 10

1475732430

ISBN 13

9781475732436

Aggregation in Large-Scale Optimization

By: I. Litvinchev,Vladimir Tsurkov

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Aggregation in Large-Scale Optimization

By: I. Litvinchev,Vladimir Tsurkov

When analyzing systems with a large number of parameters, the dimen sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The aggregated variables are either readily defined and processed, or the aggregated system may be considered as an approximate model for the orig inal system. In the latter case, the operation of the original system can be exhaustively analyzed within the framework of the aggregated model, and one faces the problems of defining the rules for introducing macrovariables, specifying loss of information and accuracy, recovering original variables from aggregates, etc. We consider also in detail the so-called iterative aggregation approach. It constructs an iterative process, at· every step of which a macroproblem is solved that is simpler than the original problem because of its lower dimension. Aggregation weights are then updated, and the procedure passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization.

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301

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Springer Science & Business Media

Published Date

ISBN 10

1441991549

ISBN 13

9781441991546

Minimax Under Transportation Constrains

By: Vladimir Tsurkov,A. Mironov

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Minimax Under Transportation Constrains

By: Vladimir Tsurkov,A. Mironov

Transportation problems belong to the domains mathematical program ming and operations research. Transportation models are widely applied in various fields. Numerous concrete problems (for example, assignment and distribution problems, maximum-flow problem, etc. ) are formulated as trans portation problems. Some efficient methods have been developed for solving transportation problems of various types. This monograph is devoted to transportation problems with minimax cri teria. The classical (linear) transportation problem was posed several decades ago. In this problem, supply and demand points are given, and it is required to minimize the transportation cost. This statement paved the way for numerous extensions and generalizations. In contrast to the original statement of the problem, we consider a min imax rather than a minimum criterion. In particular, a matrix with the minimal largest element is sought in the class of nonnegative matrices with given sums of row and column elements. In this case, the idea behind the minimax criterion can be interpreted as follows. Suppose that the shipment time from a supply point to a demand point is proportional to the amount to be shipped. Then, the minimax is the minimal time required to transport the total amount. It is a common situation that the decision maker does not know the tariff coefficients. In other situations, they do not have any meaning at all, and neither do nonlinear tariff objective functions. In such cases, the minimax interpretation leads to an effective solution.

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321

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Springer Science & Business Media

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ISBN 10

1461540607

ISBN 13

9781461540601

Control of Fluid-Containing Rotating Rigid Bodies

By: Anatoly A. Gurchenkov,Mikhail V. Nosov,Vladimir I. Tsurkov

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Control of Fluid-Containing Rotating Rigid Bodies

By: Anatoly A. Gurchenkov,Mikhail V. Nosov,Vladimir I. Tsurkov

This book is devoted to the study of the dynamics of rotating bodies with cavities containing liquid. Two basic classes of motions are analyzed: rotation and libration. Cases of complete and partial filling of cavities with ideal liquid and complete filling with viscous liquid are treated. The volume presents a method for obtaining relations between angular velocities perpendicular to main rotation and external force momentums, which are treated as control. The developed models and methods of solving dynamical problems as well as numerical methods for solving problems of optimal control can be used for studying the dynamics of aircraft in the atmosphere and spacecraft with stores of liquid fuel, which are rotating around some axis for stabilization. The results are also applicable in the development of fast revolving rotors, centrifuges and gyroscopes, which have cavities filled with liquid. This work will be of interest to researchers at universities and laboratories specializing in problems of control for hybrid systems, as well as to under-/postgraduates with this specialization. It will also benefit researchers and practitioners in aerospace and mechanical engineering.

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