Bayesian approach global optimization di mockus jonas (38 risultati)

Hardcover. Condizione: Very Good. Springer, 1997. Book has mild rubbing and smudges to covers, modest wear to edges with bumps to outer corners, light wear to head and foot of spine. otherwise in very good condition, strong binding, bright and unmarked pages; some cosmetic wear but an overall sturdy copy with clean interiors. Hard disks included in back.…

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Taschenbuch. Condizione: Neu. A Set of Examples of Global and Discrete Optimization | Applications of Bayesian Heuristic Approach | Jonas Mockus | Taschenbuch | Applied Optimization | xiv | Englisch | 2013 | Springer | EAN 9781461371144 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Et moi, . si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .'; 'One service logic has rendered com puter science . .'; 'One service category theory has rendered mathematics . .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.…

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book shows how the Bayesian Approach (BA) improves well known heuristics by randomizing and optimizing their parameters. That is the Bayesian Heuristic Approach (BHA). The ten in-depth examples are designed to teach Operations Research using Internet. Each example is a simple representation of some impor tant family of real-life problems. The accompanying software can be run by remote Internet users. The supporting web-sites include software for Java, C++, and other lan guages. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization prob lems. The techniques are evaluated in the spirit of the average rather than the worst case analysis. In this context, 'heuristics' are understood to be an expert opinion defining how to solve a family of problems of dis crete or global optimization. The term 'Bayesian Heuristic Approach' means that one defines a set of heuristics and fixes some prior distribu tion on the results obtained. By applying BHA one is looking for the heuristic that reduces the average deviation from the global optimum. The theoretical discussions serve as an introduction to examples that are the main part of the book. All the examples are interconnected. Dif ferent examples illustrate different points of the general subject. How ever, one can consider each example separately, too.…

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Condizione: Used. pp. 420 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condizione: Used. pp. 420.

Taschenbuch. Condizione: Neu. Bayesian Heuristic Approach to Discrete and Global Optimization | Algorithms, Visualization, Software, and Applications | Jonas Mockus (u. a.) | Taschenbuch | Nonconvex Optimization and Its Applications | xv | Englisch | 2010 | Springer | EAN 9781441947673 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware - Et moi, . si j'avait su comment en revcnir. One service mathematics has rendered the je o'y semis point alle.' human race. It has put common sense back Jules Verne where it beloogs. on the topmost shelf next to the dusty canister labelled 'discarded non The series is divergent; therefore we may be sense', able to do something with it. Eric T. BclI O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .'; 'One service logic has rendered com puter science . .'; 'One service category theory has rendered mathematics . .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series. 272 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 1 Global optimization and the Bayesian approach.- 1.1 What is global optimization.- 1.2 Advantages of the Bayesian approach to global optimization.- 2 The conditions of Bayesian optimality.- 2.1 Introduction.- 2.2 Reduction to dynamic programming equations.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -1 Global optimization and the Bayesian approach.- 1.1 What is global optimization .- 1.2 Advantages of the Bayesian approach to global optimization.- 2 The conditions of Bayesian optimality.- 2.1 Introduction.- 2.2 Reduction to dynamic programming equations.- 2.3 The existence of a measurable solution.- 2.4 The calculation of conditional expectations.- 2.5 The one-step approximation.- 2.6 The adaptive Bayesian approach.- 3 The axiomatic non-probabilistic justification of Bayesian optimality conditions.- 3.1 Introduction.- 3.2 The linearity of the loss function.- 3.3 The existence of the unique a priori probability corresponding to subjective preferences.- 3.4 Optimal method under uncertainty.- 3.5 Nonlinear loss functions.- 4 Stochastic models.- 4.1 Introduction.- 4.2 Sufficient convergence conditions.- 4.3 The Gaussian field.- 4.4 Homogeneous Wiener field.- 4.5 A case of noisy observations.- 4.6 Estimation of parameters from dependent observations.- 5 Bayesian methods for global optimization in the Gaussian case.- 5.1 The one-step approximation.- 5.2 Adaptive models.- 5.3 Extrapolation models.- 5.4 Maximum likelihood models.- 5.5 The comparison of algorithms.- 5.6 The Bayesian approach to global optimization with linear constraints.- 5.7 The Bayesian approach to global optimization with nonlinear constraints.- 5.8 The Bayesian approach to multi-objective optimization.- 5.9 Interactive procedures and the Bayesian approach to global optimization.- 5.10 The reduction of multi-dimensional data.- 5.11 The stopping rules.- 6 The analysis of structure and the simplification of the optimization problems.- 6.1 Introduction.- 6.2 Structural characteristics and the optimization problem.- 6.3 The estimation of structural characteristics.- 6.4 The estimation of a simplification error.- 6.5 Examples of the estimates.- 7 The Bayesian approach to local optimization.- 7.1 Introduction.- 7.2 The one-dimensional Bayesian model.- 7.3 Convergence of the local Bayesian algorithm.- 7.4 Generalization of a multi-dimensional case.- 7.5 Convergence in the multi-dimensional case.- 7.6 The local Bayesian algorithm.- 7.7 Results of computer simulation.- 8 The application of Bayesian methods.- 8.1 Introduction.- 8.2 The optimization of an electricity meter.- 8.3 The optimization of vibromotors.- 8.4 The optimization of a shock-absorber.- 8.5 The optimization of a magnetic beam deflection system.- 8.6 The optimization of small aperture coupling between a rectangular waveguide and a microstrip line.- 8.7 The maximization of LSI yield by optimization of parameters of differential amplifier functional blocks.- 8.8 Optimization of technology to avoid waste in the wet-etching of printed circuit boards in iron-copper-chloride solutions.- 8.9 The optimization of pigment compounds.- 8.10 The least square estimation of electrochemical adsorption using observations of the magnitude of electrode impedance.- 8.11 Estimation of parameters of the immunological model.- 8.12 The optimization of nonstationary queuing systems.- 8.13 The analysis of structure of the Steiner problem.- 8.14 The estimation of decision making by intuition on the example of the Steiner problem.- 9 Portable FORTRAN software for global optimization.- 9.1 Introduction.- 9.2 Parameters.- 9.3 Methods available.- 9.4 Common blocks.- 9.5 The function.- 9.6 The main program.- 9.7 The example of the main program.- 9.8 Description of routines.- 9.9 BAYES1, the global Bayesian method by Mockus.- 9.10 UNT, the global method of extrapolation type by Zilinskas.- 9.11 LPMIN, the global method of uniform search by Sobolj, Shaltenis and Dzemyda.- 9.12 GLOPT, the global method of clustering type by Törn.- 9.13 MIG1, the global method of Monte Carlo (uniform random search).- 9.14 MIG2, the modified version of MIG 1.- 9.15 EXTR, the global one-dimensional method by Zilinskas.- 9.16 MIVAR4, the local method of variable metrics by Tieshis.- 9.17 REQP, th.…