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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Computing 47 (1991), S. 29-42 
    ISSN: 1436-5057
    Keywords: 65K05 ; Interval Newton method ; interval arithmetic ; optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Die Arbeit entwickelt ein optimales Intervall-Newton-Verfahren für Systeme von nichtlinearen Gleichungen, wie sie im Zusammenhang mit nichtlinearen Optimierungsaufgaben auftreten. Die Modifikationen des Intervall-Newton-Verfahrens in dieser Arbeit stellen numerisch wirkungsvolle Verbesserungen des allgemeinen Intervall-Newton-Verfahrens dar. In der Arbeit wird aufgezeigt, daß die Berechnung einer optimalen Schrittweite im Intervall-Newton-Verfahren notwendig ist, damit man die Generierung einer feiner werdenden Einschließungsfolge garantieren kann. Dieses Vorgehen wird als optimales Newton-Verfahren bezeichnet und für den mehrdimensionalen Fall implementiert. Die Arbeit zeigt dann die Verwendung des optimalen Intervall-Newton-Verfahrens als “feasible direction”-Verfahren im Zusammenhang mit Nichtnegativitätsbedingungen. In der Implementierung wird auch eine Technik der Matrizenzerlegung verwendet, die den Rechenaufwand für die Inverse der Hesseschen Matrix im Intervall-Newton-Verfahren vermindert. Das Vorgehen wird an mehreren Beispielen demonstriert, u.a. an Optimierungsaufgaben mit Störungen der Problemkoeffizienten. Die numerischen Ergebnisse bestätigen die Leistungsfähigkeit unserer Ansätze.
    Notes: Abstract This paper presents the development of an optimal interval Newton method for systems of nonlinear equations. The context of solving such systems of equations is that of optimization of nonlinear mathematical programs. The modifications of the interval Newton method presented in this paper provide computationally effective enhancements to the general interval Newton method. The paper demonstrates the need to compute an optimal step length in the interval Newton method in order to guarantee the generation of a sequence of improving solutions. This method is referred to as the optimal Newton method and is implemented in multiple dimensions. Secondly, the paper demonstrates the use of the optimal interval Newton method as a feasible direction method to deal with non-negativity constraints. Also, included in this implementation is the use of a matrix decomposition technique to reduce the computational effort required to compute the Hessian inverse in the interval Newton method. The methods are demonstrated on several problems. Included in these problems are mathematical programs with perturbations in the problem coefficients. The numerical results clearly demonstrate the effectiveness and efficiency of these approaches.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 13 (1977), S. 200-220 
    ISSN: 1436-4646
    Keywords: Cutting-plane algorithms ; Geometric programming ; Computational results ; Constrained optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper presents the results of computational studies of the properties of cutting plane algorithms as applied to posynomial geometric programs. The four cutting planes studied represent the gradient method of Kelley and an extension to develop tangential cuts; the geometric inequality of Duffin and an extension to generate several cuts at each iteration. As a result of over 200 problem solutions, we will draw conclusions regarding the effectiveness of acceleration procedures, feasible and infeasible starting point, and the effect of the initial bounds on the variables. As a result of these experiments, certain cutting plane methods are seen to be attractive means of solving large scale geometric programs.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 17 (1979), S. 109-113 
    ISSN: 1436-4646
    Keywords: Geometric Programs ; Kuhn—Tucker Conditions ; Condensation Methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The purpose of this paper is to present some points of clarification of a recently presented algorithm for geometric programs [7]. While presenting the clarification, we are able to identify the behavior of condensation type algorithms for generalized geometric programs.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 7 (1974), S. 181-190 
    ISSN: 1436-4646
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract An algorithm for solving ordinary geometric programs is presented. The algorithm is based on the reduced system associated with geometric programs and is highly flexible in that it allows the use of several nonlinear optimization techniques.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 6 (1974), S. 114-116 
    ISSN: 1436-4646
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 61 (1993), S. 377-384 
    ISSN: 1436-4646
    Keywords: Perturbed convex programs ; interval analysis ; sensitivity analysis ; nonlinear programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Several authors have used interval arithmetic to deal with parametric or sensitivity analysis in mathematical programming problems. Several reported computational experiments have shown how interval arithmetic can provide such results. However, there has not been a characterization of the resulting solution interval in terms of the usual sensitivity analysis results. This paper presents a characterization of perturbed convex programs and the resulting solution intervals. Interval arithmetic was developed as a mechanism for dealing with the inherent error associated with numerical computations using a computational device. Here it is used to describe error in the parameters. We show that, for convex programs, the resulting solution intervals can be characterized in terms of the usual sensitivity analysis results. It has been often reported in the literature that even well behaved convex problems can exhibit pathological behavior in the presence of data perturbations. This paper uses interval arithmetic to deal with such problems, and to characterize the behavior of the perturbed problem in the resulting interval. These results form the foundation for future computational studies using interval arithmetic to do nonlinear parametric analysis.
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Policy sciences 9 (1978), S. 87-96 
    ISSN: 1573-0891
    Source: Springer Online Journal Archives 1860-2000
    Topics: Political Science , Economics
    Notes: Abstract This research demonstrates the need for and the usefulness of a multi-dimensional approach to the management of environmental programs. While the thrust of the paper is toward environmental quality programs the conceptual development is appropriate for many public sector programs. The paper describes an experiment in the determination of multi-dimensional objectives within three programs in a state environmental quality program. In addition, measures of effectiveness, based on the multi-dimensional objectives, are proposed as relevant measures of program effectiveness.
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 20 (1989), S. 283-302 
    ISSN: 1572-9338
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper presents the use of surrogate constraints and Lagrange multipliers to generate advanced starting solutions to constrained network problems. The surrogate constraint approach is used to generate a singly constrained network problem which is solved using the algorithm of Glover, Karney, Klingman and Russell [13]. In addition, we test the use of the Lagrangian function to generate advanced starting solutions. In the Lagrangian approach, the subproblems are capacitated network problems which can be solved using very efficient algorithms. The surrogate constraint approach is implemented using the multiplier update procedure of Held, Wolfe and Crowder [16]. The procedure is modified to include a search in a single direction to prevent periodic regression of the solution. We also introduce a reoptimization procedure which allows the solution from thekth subproblem to be used as the starting point for the next surrogate problem for which it is infeasible once the new surrogate constraint is adjoined. The algorithms are tested under a variety of conditions including: large-scale problems, number and structure of the non-network constraints, and the density of the non-network constraint coefficients. The testing clearly demonstrates that both the surrogate constraint and Langrange multipliers generate advanced starting solutions which greatly improve the computational effort required to generate an optimal solution to the constrained network problem. The testing demonstrates that the extra effort required to solve the singly constrained network subproblems of the surrogate constraints approach yields an improved advanced starting point as compared to the Lagrangian approach. It is further demonstrated that both of the relaxation approaches are much more computationally efficient than solving the problem from the beginning with a linear programming algorithm.
    Type of Medium: Electronic Resource
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  • 9
    Publication Date: 1989-12-01
    Print ISSN: 0254-5330
    Electronic ISSN: 1572-9338
    Topics: Mathematics , Economics
    Published by Springer
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  • 10
    Publication Date: 1991-03-01
    Print ISSN: 0010-485X
    Electronic ISSN: 1436-5057
    Topics: Computer Science
    Published by Springer
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