The results of optimistic model and pessimistic model are always not efficient for practical application because they are two extreme possibilities for ill-posed bilevel programming problem. This paper presents a new ...
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(纸本)9780769550169
The results of optimistic model and pessimistic model are always not efficient for practical application because they are two extreme possibilities for ill-posed bilevel programming problem. This paper presents a new model by transferring Minmax model to a Maxmin model to solve above shortcoming. And we prove, by this transformation, a more practical value between the optimistic value and the pessimistic value can be achieved. Then, a new descent algorithm is developed to solve this new model by considering the satisfying degree of the lower level. Finally, an illustrated numerical example is demonstrated to show the proposed new model and algorithm are feasible and we can get a better result from all the iterated points by this Maxmin model than by the two extreme models.
An approach for solving quasi-equilibrium problems (QEPs) is proposed relying on gap functions, which allow reformulating QEPs as global optimization problems. The (generalized) smoothness properties of a gap function...
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An approach for solving quasi-equilibrium problems (QEPs) is proposed relying on gap functions, which allow reformulating QEPs as global optimization problems. The (generalized) smoothness properties of a gap function are analysed and an upper estimate of its Clarke directional derivative is given. Monotonicity assumptions on both the equilibrium and constraining bifunctions are a key tool to guarantee that all the stationary points of a gap function actually solve QEP. A few classes of constraints satisfying such assumptions are identified covering a wide range of situations. Relying on these results, a descent method for solving QEP is devised and its convergence proved. Finally, error bounds are given in order to guarantee the boundedness of the sequence generated by the algorithm.
In this paper, an optimal control problem for glass cooling processes is studied. We model glass cooling using the SP1 approximations to the radiative heat transfer equations. The control variable is the temperature a...
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In this paper, an optimal control problem for glass cooling processes is studied. We model glass cooling using the SP1 approximations to the radiative heat transfer equations. The control variable is the temperature at the boundary of the domain. This results in a boundary control problem for a parabolic/elliptic system which is treated by a constrained optimization approach. We consider several cost functionals of tracking-type and formally derive the first-order optimality system. Several numerical methods based on the adjoint variables are investigated. We present results of numerical simulations illustrating the feasibility and performance of the different approaches. Copyright (C) 2004 John Wiley Sons, Ltd.
We consider optimal design problems for semiconductor devices which are simulated using the energy transport model. We develop a descent algorithm based on the adjoint calculus and present numerical results for a ball...
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We consider optimal design problems for semiconductor devices which are simulated using the energy transport model. We develop a descent algorithm based on the adjoint calculus and present numerical results for a ballistic diode. Furthermore, we compare the optimal doping pro. le with results computed based on the drift diffusion model. Finally, we exploit the model hierarchy and test the space mapping approach, especially the aggressive space mapping algorithm, for the design problem. This yields a significant reduction of numerical costs and programming effort.
In this paper, a new spectral PRP conjugate gradient algorithm has been developed for solving unconstrained optimization problems, where the search direction was a kind of combination of the gradient and the obtained ...
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In this paper, a new spectral PRP conjugate gradient algorithm has been developed for solving unconstrained optimization problems, where the search direction was a kind of combination of the gradient and the obtained direction, and the steplength was obtained by the Wolfe-type inexact line search. It was proved that the search direction at each iteration is a descent direction of objective function. Under mild conditions, we have established the global convergence theorem of the proposed method. Numerical results showed that the algorithm is promising, particularly, compared with the existing several main methods. (C) 2010 Elsevier Ltd. All rights reserved.
A globally convergent algorithm is presented for the solution of a wide class of semi-infinite programming problems. The method is based on the solution of a sequence of equality constrained quadratic programming prob...
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A globally convergent algorithm is presented for the solution of a wide class of semi-infinite programming problems. The method is based on the solution of a sequence of equality constrained quadratic programming problems, and usually has a second order convergence rate. Numerical results illustrating the method are given.
A new conjugate gradient method is proposed by applying Powell's symmetrical technique to conjugate gradient methods in this paper. Using Wolfe line searches, the global convergence of the method is analyzed by us...
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A new conjugate gradient method is proposed by applying Powell's symmetrical technique to conjugate gradient methods in this paper. Using Wolfe line searches, the global convergence of the method is analyzed by using the spectral analysis of the conjugate gradient iteration matrix and Zoutendijk's condition. Based on this, some concrete descent algorithms are developed. 200s numerical experiments are presented to verify their performance and the numerical results show that these algorithms are competitive compared with the PRP(+) algorithm. Finally, a brief discussion of the new proposed method is given.
It is true that in all-optical networks, network performance can be improved by wavelength conversion. However, the switching node with wavelength conversion capability is still costly, and the number of such nodes sh...
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It is true that in all-optical networks, network performance can be improved by wavelength conversion. However, the switching node with wavelength conversion capability is still costly, and the number of such nodes should be limited in the network. In this paper, a performance optimization problem is treated in all-optical networks, We propose a heuristic algorithm to minimize an overall blocking probability by properly allocating a limited number of nodes with wavelength conversion capability. The routing strategy is also considered suitable to the case where the number of wavelength convertible nodes are limited. We validate the minimization level of our heuristic algorithm through numerical examples, and show that our algorithm can properly allocate nodes with conversion and decide routes for performance optimization.
In this paper, an improved spectral conjugate gradient algorithm is developed for solving nonconvex unconstrained optimization problems. Different from the existent methods, the spectral and conjugate parameters are c...
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In this paper, an improved spectral conjugate gradient algorithm is developed for solving nonconvex unconstrained optimization problems. Different from the existent methods, the spectral and conjugate parameters are chosen such that the obtained search direction is always sufficiently descent as well as being close to the quasi-Newton direction. With these suitable choices, the additional assumption in the method proposed by Andrei on the boundedness of the spectral parameter is removed. Under some mild conditions, global convergence is established. Numerical experiments are employed to demonstrate the efficiency of the algorithm for solving large-scale benchmark test problems, particularly in comparison with the existent state-of-the-art algorithms available in the literature.
In this work we give sufficient conditions for the finite convergence of descent algorithms for solving variational inequalities involving generalized monotone mappings.
In this work we give sufficient conditions for the finite convergence of descent algorithms for solving variational inequalities involving generalized monotone mappings.
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