Using the so-called aggregate function of the constraints, a new aggregate constraint homotopy (ACH) is constructed and corresponding interior path following method for smooth programming is proposed. It was proved th...
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Using the so-called aggregate function of the constraints, a new aggregate constraint homotopy (ACH) is constructed and corresponding interior path following method for smooth programming is proposed. It was proved that under a weak normal cone condition, the ACH determines a smooth interior path from a given interior point to a K-K-T point. This forms the theoretical base of ACH method.
The optimal control theory can be applied to solve the optimization problems of dynamic system. Two major approaches which are used commonly to solve optimal control problems (OCP) are discussed in this paper. A numer...
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ISBN:
(纸本)0889864241
The optimal control theory can be applied to solve the optimization problems of dynamic system. Two major approaches which are used commonly to solve optimal control problems (OCP) are discussed in this paper. A numerical method based on discretization and nonlinear programming techniques is proposed and implemented an OCP solver. In addition, a systematic procedure for solving optimal control problems by using the OCP solver is suggested. Two various types of OCP, A flight level tracking problem and minimum time problem, are modeled according the proposed NLP formulation and solved by applying the OCP solver. The results reveal that the proposed method constitutes a viable method for solving optimal control problems.
A method for solving optimization problem with continuous parameters using improved ant colony algorithm is presented. In the method, groups of candidate values of the components are constructed, and each value in the...
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ISBN:
(纸本)3540239871
A method for solving optimization problem with continuous parameters using improved ant colony algorithm is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then, crossover and mutation can determine the values of the components in the solution. Our experimental results of the problem of nonlinear programming show that our method has much higher convergence speed and stability than that of GA, and the drawback of ant colony algorithm of not being suitable for solving continuous optimization problems is overcome.
Presents a study on the NOP-2 modeling language for nonlinear programming. Definition of the modeling language for specifying general optimization problems; Effective solution for global optimization problems; Local o...
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Presents a study on the NOP-2 modeling language for nonlinear programming. Definition of the modeling language for specifying general optimization problems; Effective solution for global optimization problems; Local optimization programs which do not need analytical knowledge for a fast solving process.
In this paper we describe how robot trajectory planning can be formulated as a semi-infinite programming (SIP) problem. The formulation as a SIP problem allowed us to treat the problem with one of the three main class...
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In this paper we describe how robot trajectory planning can be formulated as a semi-infinite programming (SIP) problem. The formulation as a SIP problem allowed us to treat the problem with one of the three main classes of methods for solving SIP, the discretization class. Two of the robotics trajectory planning problems formulated were coded in the SIPAMPL environment which is publicly available. A B-Spline library was also created to allow the codification of the robotics trajectory problem. (C) 2003 Elsevier B.V. All rights reserved.
The security of Grid sites can be enhanced by upgrading its intrusion defense capabilities against its previous job success rate on Grid platforms. A new fuzzy-logic trust model is proposed for distributed security en...
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ISBN:
(纸本)1880843528
The security of Grid sites can be enhanced by upgrading its intrusion defense capabilities against its previous job success rate on Grid platforms. A new fuzzy-logic trust model is proposed for distributed security enforcement across multiple Grid resources sites. The design is aimed at securing Grid resources with optimized resources subject to budget constraints. The performance of trusted Grid computing is verified by simulated trust integration over multiple Grid resource sites. The SARAH scheme scales well with increasing number of divisible user jobs and can sustain high efficiency, as more resource sites are added. Integrated trust and resource optimization make it possible to accommodate all user applications with low job drops and short waiting time. As a result, the Grid resources are better utilized for distributed execution of large number of user jobs.
We show that the effects of finite-precision arithmetic in forming and solving the linear system that arises at each iteration of primal-dual interior-point algorithms for nonlinear programming are benign, provided th...
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We show that the effects of finite-precision arithmetic in forming and solving the linear system that arises at each iteration of primal-dual interior-point algorithms for nonlinear programming are benign, provided that the iterates satisfy centrality and feasibility conditions of the type usually associated with path-following methods. When we replace the standard assumption that the active constraint gradients are independent by the weaker Mangasarian-Fromovitz constraint qualification, rapid convergence usually is attainable, even when cancellation and roundoff errors occur during the calculations. In deriving our main results, we prov a key technical result about the size of the exact primal-dual step. This result can be used to modify existing analysis of primal-dual interior-point methods for convex programming, making it possible to extend the superlinear local convergence results to the nonconvex case.
An efficient new SQP algorithm capable of solving large-scale problems is described. It generates descent directions for an l(1) plus log-barrier merit function and uses a line-search to obtain a sufficient decrease o...
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An efficient new SQP algorithm capable of solving large-scale problems is described. It generates descent directions for an l(1) plus log-barrier merit function and uses a line-search to obtain a sufficient decrease of this function. The unmodified exact Hessian matrix of the Lagrangian function is normally used in the QP subproblem, but this is set to zero if it fails to yield a descent direction for the merit function. The QP problem is solved by an interior-point method using an inexact Newton approach, iterating to an accuracy just sufficient to produce a descent direction in the early stages and tightening the accuracy as we approach a solution. We prove finite termination of the algorithm, at an epsilon -optimal Fritz-John point if feasibility is attained. We also show that if any iterate is close enough to an isolated connected subset of local minimizers, then the iterates converge to this subset. The rate of convergence is Q-quadratic if the subset is an isolated minimizer which satis es a second-order sufficiency condition, but Q-quadratic convergence to an epsilon -optimal point can still be achieved without any conditions beyond Lipschitz continuity of second-order derivatives. The implementation SQPIPM is designed for problems with many degrees of freedom and is shown to perform well compared with other codes on a range of standard problems.
The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications in efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear...
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The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications in efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear production function is carried out. The results suggest that ANNs are a promising alternative to traditional approaches, econometric models and non-parametric methods such as data envelopment analysis, to fit production functions and measure efficiency under non-linear contexts.
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