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Asignación de recursos Max-Min: propiedades y algoritmos.

Amparo Mármol Conde, Blas Pelegrín Pelegrín (1991)

Trabajos de Investigación Operativa

Este trabajo trata el problema de asignación de recursos cuando el objetivo es maximizar la mínima recompensa y las funciones recompensa son continuas y estrictamente crecientes. Se estudian diferentes propiedades que conducen a algoritmos que permiten de forma eficiente la resolución de gran variedad de problemas de esta naturaleza, tanto con variables continuas como discretas.

Asymptotic behavior of second-order dissipative evolution equations combining potential with non-potential effects

Hedy Attouch, Paul-Émile Maingé (2011)

ESAIM: Control, Optimisation and Calculus of Variations

In the setting of a real Hilbert space , we investigate the asymptotic behavior, as time t goes to infinity, of trajectories of second-order evolution equations            ü(t) + γ u ˙ (t) + ∇ϕ(u(t)) + A(u(t)) = 0, where ∇ϕ is the gradient operator of a convex differentiable potential function ϕ: ,A: is a maximal monotone operator which is assumed to beλ-cocoercive, and γ > 0 is a damping parameter. Potential and non-potential effects are associated respectively to ∇ϕ and A. Under condition...

Asymptotic behavior of second-order dissipative evolution equations combining potential with non-potential effects*

Hedy Attouch, Paul-Émile Maingé (2011)

ESAIM: Control, Optimisation and Calculus of Variations

In the setting of a real Hilbert space , we investigate the asymptotic behavior, as time t goes to infinity, of trajectories of second-order evolution equations            ü(t) + γ u ˙ (t) + ∇ϕ(u(t)) + A(u(t)) = 0, where ∇ϕ is the gradient operator of a convex differentiable potential function ϕ : , A : is a maximal monotone operator which is assumed to be λ-cocoercive, and γ > 0 is a damping parameter. Potential and non-potential effects are associated respectively to ∇ϕ and A. Under condition...

Asymptotische Berührung k -ter Ordnung konvexer Mengen

Libuše Grygarová (1980)

Aplikace matematiky

In der Arbeit geht es um die Charakteristik des allgemeinen Begriffs der asymptotischen Berührung von solchen abgeschlossenen, konvexen Mengen in E n , wo ihr Abstand gleich Null und ihr Durchschnitt leer ist. Es wird gezeigt, dass unter diesem Umstand man dem fraglichen Mengenpaar ein Tripel von natürlichen Zahlen (die Ordnung der Berührung, der Grad der Berührung und die Diemnsion des zugehörigen asymptotischen, linearen Raumes), welches eine Charakteristik dieser Berührung darstellt, eindeutig zuordnen...

Chance constrained optimal beam design: Convex reformulation and probabilistic robust design

Jakub Kůdela, Pavel Popela (2018)

Kybernetika

In this paper, we are concerned with a civil engineering application of optimization, namely the optimal design of a loaded beam. The developed optimization model includes ODE-type constraints and chance constraints. We use the finite element method (FEM) for the approximation of the ODE constraints. We derive a convex reformulation that transforms the problem into a linear one and find its analytic solution. Afterwards, we impose chance constraints on the stress and the deflection of the beam....

Configuring a sensor network for fault detection in distributed parameter systems

Maciej Patan, Dariusz Uciński (2008)

International Journal of Applied Mathematics and Computer Science

The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among...

Conjugate gradient algorithms for conic functions

Ladislav Lukšan (1986)

Aplikace matematiky

The paper contains a description and an analysis of two modifications of the conjugate gradient method for unconstrained minimization which find a minimum of the conic function after a finite number of steps. Moreover, further extension of the conjugate gradient method is given which is based on a more general class of the model functions.

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