In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients...
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In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients of the active constraints, we analyze the global convergence of the proposed algorithm.
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger the dependence on efficient and scalable software is severe. Toolkit fo...
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ISBN:
(纸本)076952138X
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger the dependence on efficient and scalable software is severe. Toolkit for Advanced optimization (TAO) is a parallel package, that can currently solve several kinds of optimization problems. In this paper we give the framework of several variants of CG: CG_FR, CG_PR, CG_PRP and their implementations in TAO1.5, which have been tested up to 64 processors on Dawning2000 to solve problems with up to 10(6) variables. The results show that the scalability of CG implementations in TAO1.5 is excellent.
This paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENO...
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ISBN:
(纸本)0889864241
This paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENOSIM-p is the parallel version of previous optimizationsoftware GENOSIM [1]. GENOSIM-p employs PGA to calibrate traffic microsimulation models. In this research, we will use PARAMICS: a microscopic traffic simulation platform. PARAMICS consists of highperformance cross-linked traffic models having multiple user-adjustable parameters. GENOSIM-p will use PGA to manipulate those control parameters and search for an optimal set of values that minimize the discrepancy between simulation output and real field data.
This paper is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches whic...
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ISBN:
(纸本)0780383532
This paper is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches which optimize the underlying fuzzy sets, we will mainly focus on the linear approximation of the output variable according to the input data. While the first approach leads to nonlinear problems, the latter will result in a free, linear least squares system to be solved. Therefore this approach can be used for high dimensional problems as well, when due to the increasing complexity nonlinear systems are no longer applicable. By applying Tikhonov regularization we get stable and fast algorithms that create sufficiently optimized controllers;with saving their interpretability. Finally we will show, how variable selection can be used to increase interpretability and to reduce computation time.
An improved evolutionary algorithm is proposed to perform multiobjective dynamic optimization of a semi-batch styrene polymerization process. The target is to determine the optimal feeding trajectories and the reactor...
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An improved evolutionary algorithm is proposed to perform multiobjective dynamic optimization of a semi-batch styrene polymerization process. The target is to determine the optimal feeding trajectories and the reactor operating temperature, which maximize the monomer conversion rate and minimize the initiator residue concentration in the final product, The optimization problem has been formulated as a multi-objective mixed-integer nonlinear problem (MOMINLP). The proposed approach allows the effective computation of the optimal operating strategies for the production of polymers with the average molecular weight and the polydispersity index required.
This paper introduces a hybrid optimization algorithm, followed by a corresponding estimation technique, for the estimation of ARMAX models. The hybrid algorithm consists of a stochastic component and a deterministic ...
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ISBN:
(纸本)0791841731
This paper introduces a hybrid optimization algorithm, followed by a corresponding estimation technique, for the estimation of ARMAX models. The hybrid algorithm consists of a stochastic component and a deterministic counterpart and aims at combining high convergence rate together with reliability in the search for global optimum. The estimation procedure is slit in two phases, due to the mixed linear-nonlinear relationship between the residuals and the parameter vector, and results in stable and invertible models. The proposed methodology is implemented in the estimation of a half-car suspension model of a road vehicle, using noise-corrupted observations, and the results yield very stable performance of the hybrid algorithm, reduced computational cost, in comparison to conventional stochastic optimizationalgorithms, and ability to describe satisfactory system's dynamics.
This paper introduces the architecture and algorithms of TCMiner: a highperformance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions. The system has the following c...
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ISBN:
(纸本)3540237224
This paper introduces the architecture and algorithms of TCMiner: a highperformance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions. The system has the following competing advantages: (1) highperformance (2) Multi-dimensional Data Analysis Capability (3) high Flexibility (4) Powerful Interoperability (5) Special optimization for TCM. This data mining system can work as a powerful assistant for TCM experts by conducting Traditional Chinese Medicine Data Mining such as Computer-Aided Medicine Pairing Analysis, Medicine Syndrome Correlation, Quality and Flavor Trend Analysis, and Principal Components Analysis and Prescriptions Reduction etc.
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger, the dependence on efficient and scalable software is severe. Toolkit f...
详细信息
ISBN:
(纸本)076952138X
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger, the dependence on efficient and scalable software is severe. Toolkit for Advanced optimization (TAO) is a parallel package that can currently solve several kinds of optimization problems. In this paper, we give the framework of several variants of CG: CGFR, CGPR, CGPRP and their implementations in TAO 1.5, which have been tested up to 64 processors on Dawning2000 to solve problems with up to 10 variables. The results show that the scalability of CG implementations in TAO 1.5 is excellent.
Scalar replacement is an effective optimization for removing memory accesses. However, exposing all possible array reuse with scalars may cause a significant increase in register pressure, resulting in register spilli...
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Scalar replacement is an effective optimization for removing memory accesses. However, exposing all possible array reuse with scalars may cause a significant increase in register pressure, resulting in register spilling and performance degradation. We present a low cost method to predict the register pressure of a loop before applying scalar replacement on high-level source code, called pseudo-schedule register prediction (PRP), that takes into account the effects of both software pipelining and register allocation. PRP attempts to eliminate the possibility of degradation from scalar replacement due to register spilling while providing opportunities for a good speedup. PRP uses three approximation algorithms: one for constructing a data dependence graph, one for computing the recurrence constraints of a software pipelined loop, and one for building a pseudo-schedule. Our experiments show that PRP predicts the floating-point register pressure within 2 registers and the integer register pressure within 2.7 registers on average with a time complexity of O(n/sup 2/) in practice. PRP achieves similar performance to the best previous approach, having O(n/sup 3/) complexity, with less than one-fourth of the compilation time on our test suite.
This work is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches which...
详细信息
This work is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches which optimize the underlying fuzzy sets, we mainly focus on the linear approximation of the output variable according to the input data. While the first approach leads to nonlinear problems, the latter results in a free, linear least squares system to be solved. Therefore this approach can be used for high dimensional problems as well, when due to the increasing complexity nonlinear systems are no longer applicable. By applying Tikhonov regularization we get stable and fast algorithms that create sufficiently optimized controllers; with saving their interpretability. Finally we show, how variable selection can be used to increase interpretability and to reduce computation time.
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