Sensitivity Analysis in Parametric Nonlinear Programming PDF Download
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Author: Robert Leo Armacost
Publisher:
ISBN:
Category : Nonlinear programming
Languages : en
Pages : 508
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Book Description
Author: Robert Leo Armacost
Publisher:
ISBN:
Category : Nonlinear programming
Languages : en
Pages : 508
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Book Description
Author: Robert L. Armacost
Publisher:
ISBN:
Category : Inventory control
Languages : en
Pages : 44
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Book Description
Recently, it has been shown that a class of penalty function algorithms can readily be adapted to generate sensitivity analysis information for a large class of parametric nonlinear programming problems. In particular, estimates of the partial derivatives (with respect to the problem parameters) of the components of a solution vector and the optimal value function have been successfully calculated for a number of nontrivial examples. The approach has been implemented using the well known Sequential Unconstrained Minimization Technique (SUMT) computer program. This paper, a continuation and amplification of a recent paper by Armacost, gives a detailed summary of the significant underlying theoretical results, reviews recent additions to the computer program that include Lagrange multiplier sensitivity calculations, and elaborates on the kind of information that can be generated by further analyzing and interpreting results obtained in applying the techique to a well known inventory model. (Author).
Author: Tomas Gal
Publisher: Springer Science & Business Media
ISBN: 1461561035
Category : Business & Economics
Languages : en
Pages : 595
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Book Description
The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.
Author: Martin Grötschel
Publisher: Springer Science & Business Media
ISBN: 3662043319
Category : Mathematics
Languages : en
Pages : 789
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Book Description
In its thousands of years of history, mathematics has made an extraordinary ca reer. It started from rules for bookkeeping and computation of areas to become the language of science. Its potential for decision support was fully recognized in the twentieth century only, vitally aided by the evolution of computing and communi cation technology. Mathematical optimization, in particular, has developed into a powerful machinery to help planners. Whether costs are to be reduced, profits to be maximized, or scarce resources to be used wisely, optimization methods are available to guide decision making. Opti mization is particularly strong if precise models of real phenomena and data of high quality are at hand - often yielding reliable automated control and decision proce dures. But what, if the models are soft and not all data are around? Can mathematics help as well? This book addresses such issues, e. g. , problems of the following type: - An elevator cannot know all transportation requests in advance. In which order should it serve the passengers? - Wing profiles of aircrafts influence the fuel consumption. Is it possible to con tinuously adapt the shape of a wing during the flight under rapidly changing conditions? - Robots are designed to accomplish specific tasks as efficiently as possible. But what if a robot navigates in an unknown environment? - Energy demand changes quickly and is not easily predictable over time. Some types of power plants can only react slowly.
Author: Anthony V. Fiacco
Publisher: North Holland
ISBN:
Category : Mathematics
Languages : en
Pages : 252
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Book Description
Author: Tomas Gal
Publisher: Walter de Gruyter
ISBN: 3110871203
Category : Computers
Languages : en
Pages : 465
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Book Description
Postoptimal Analyses, Parametric Programming, and Related Topics: Degeneracy, Multicriteria Decision Making Redundancy.
Author: Malamas Caracotsios
Publisher:
ISBN:
Category :
Languages : en
Pages : 424
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Book Description
Author: Fiacco
Publisher: Academic Press
ISBN: 0080956718
Category : Computers
Languages : en
Pages : 381
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Book Description
Introduction to Sensitivity and Stability Analysis in Nonlinear Programming
Author: Robert L. Armacost
Publisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages : 59
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Book Description
Pursuing a number of theoretical results recently obtained by Fiacco, this paper continues the development of a basis for calculating first-order changes in a Kuhn-Tucker triple and second-order changes in the optimal value function of a class of general parametric nonlinear programming problems, with respect to a perturbation of the problem parameters. Exploiting problem structure, specific formulas are derived for calculating the first partial derivatives of a Kuhn-Tucker triple. Approximations to these quantities are obtained in parallel throughout, by way of an associated logarithmic-quadratic penalty function. Applications are indicated.
Author: Anthony V. Fiacco
Publisher:
ISBN:
Category : Approximation theory
Languages : en
Pages : 64
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Book Description
In the paper the author establishes a theoretical basis for using a penalty-function method to estimate sensitivity information (i.e., the partial derivatives) of a local solution and its associated Lagrange multipliers of a large class of nonlinear programming problems with respect to a general parametric variation in the problem functions. The local solution is assumed to satisfy the second order sufficient conditions for a strict minimum. Although theoretically valid for higher order derivatives, tha analysis concentrates on the estimation of the first order (first partial derivative) sensitivity information, which can be explicitly expressed in terms of the problem functions. For greater clarity, the results are given in terms of the mixed logarithmic-barrier quadratic-loss function. However, the approach is clearly applicable to any twice-differentiable penalty-function algorithm. (Author).
