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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
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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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
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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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
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No. 9781461546672. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten. Details...
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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… Mehr…
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. Books > Mathematics eBook, Springer Shop<
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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… Mehr…
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., Springer<
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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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
No. 9781461546672. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten.
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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
No. 9781461546672. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten.
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… Mehr…
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.; PDF; Reference > Research & information: general > Information theory > Cybernetics & systems theory, Springer US<
No. 9781461546672. Versandkosten:Instock, Despatched same working day before 3pm, zzgl. Versandkosten.
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… Mehr…
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. Books > Mathematics eBook, Springer Shop<
new in stock. Versandkosten:zzgl. Versandkosten. (EUR 0.00)
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… Mehr…
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., Springer<
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Detailangaben zum Buch - Hierarchical Optimization and Mathematical Physics
EAN (ISBN-13): 9781461546672 Erscheinungsjahr: 2013 Herausgeber: Springer Science+Business Media
Buch in der Datenbank seit 2017-05-23T01:40:48+02:00 (Berlin) Detailseite zuletzt geändert am 2024-02-21T23:59:00+01:00 (Berlin) ISBN/EAN: 9781461546672
ISBN - alternative Schreibweisen: 978-1-4615-4667-2 Alternative Schreibweisen und verwandte Suchbegriffe: Titel des Buches: mathematical physics
Daten vom Verlag:
Autor/in: Vladimir Tsurkov Titel: Applied Optimization; Hierarchical Optimization and Mathematical Physics Verlag: Springer; Springer US 310 Seiten Erscheinungsjahr: 2013-11-21 New York; NY; US Sprache: Englisch 96,29 € (DE) 99,00 € (AT) 118,00 CHF (CH) Available X, 310 p.
EA; E107; eBook; Nonbooks, PBS / Mathematik/Sonstiges; Kybernetik und Systemtheorie; Verstehen; Optimal control; mathematical physics; operations research; optimization; optimization theory; C; Systems Theory, Control; Optimization; Calculus of Variations and Optimization; Applications of Mathematics; Quantitative Economics; Mathematics and Statistics; Optimierung; Angewandte Mathematik; Wirtschaftstheorie und -philosophie; BC
Preface. 1. The Main Model and Constructions of the Decomposition Method. 2. Generalization of the Decomposition Approach to Mathematical Programming and Classical Calculus of Variations. 3. Hierarchical Systems of Mathematical Physics. 4. Effectiveness of Decomposition. 5. Appendix. The Main Approaches in Hierarchical Optimization. Index.
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