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 paper, a new inexact line search strategy is proposed, where a rough step length is firstly obtained by implementing the steps of backtracking in the Armijo line search, then it is improved by incorporating th...
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In this paper, a new inexact line search strategy is proposed, where a rough step length is firstly obtained by implementing the steps of backtracking in the Armijo line search, then it is improved by incorporating the features of the step length satisfying the approximate Wolfe condition. On the basis of the new line search, a modified Newton algorithm is developed to solve the unconstrained optimization problems. Under some suitable assumptions, the global convergence theory is established. Numerical experiments demonstrate that the new algorithm is effective, especially in comparison with the similar methods available in the literature.
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 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.
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, 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.
In this paper, we give a descent algorithm for solving quadratic bilevel programming problems. It is proved that the descent algorithm finds a locally optimal solution to a quadratic bilevel programming problem in a ...
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In this paper, we give a descent algorithm for solving quadratic bilevel programming problems. It is proved that the descent algorithm finds a locally optimal solution to a quadratic bilevel programming problem in a finite number of iterations. Two numerical examples are given to illustrate this algorithm.
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 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.
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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ISBN:
(纸本)0819426636
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 streategy 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 for performance optimizaition.
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