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Your search keyword '"Constrained optimization"' showing total 3 results
3 results on '"Constrained optimization"'

1. Efficient tridiagonal preconditioner for the matrix-free truncated Newton method.

Author

Lukšan, Ladislav and Vlček, Jan

Subjects
*MATRICES (Mathematics), *NEWTON-Raphson method, *CONSTRAINED optimization, *NUMERICAL analysis, *MATHEMATICAL proofs, *MATHEMATICAL analysis
Abstract

Abstract: In this paper, we study an efficient tridiagonal preconditioner, based on the directional differentiation, applied to the matrix-free truncated Newton method for unconstrained optimization. It is proved that this preconditioner is positive definite for many practical problems. The efficiency of the resulting matrix-free truncated Newton method is demonstrated by results of extensive numerical experiments. [Copyright &y& Elsevier]

Published
2014
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2. A nonmonotone adaptive trust region method for unconstrained optimization based on conic model

Author

Zhang, Jian, Zhang, Kecun, and Qu, Shaojian

Subjects
*MONOTONE operators, *CONSTRAINED optimization, *ADAPTIVE computing systems, *NUMERICAL analysis, *STOCHASTIC convergence, *MATHEMATICAL analysis
Abstract

Abstract: In this paper, we present a nonmonotone adaptive trust region method for unconstrained optimization based on conic model. The new method combines nonmonotone technique and a new way to determine trust region radius at each iteration. The local and global convergence properties are proved under reasonable assumptions. Numerical experiments show that our algorithm is effective. [ABSTRACT FROM AUTHOR]

Published
2010
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3. A nonmonotone globalization algorithm with preconditioned gradient path for unconstrained optimization

Author

Zhou, Qunyan, Sun, Wenyu, and Qi, Liqun

Subjects
*MONOTONE operators, *GLOBAL analysis (Mathematics), *CONSTRAINED optimization, *ALGORITHMS, *STOCHASTIC convergence, *NUMERICAL analysis, *MATHEMATICAL analysis
Abstract

Abstract: The aim of this paper is to incorporate the preconditioned gradient path in a nonmonotone stabilization algorithm for unconstrained optimization. The global convergence and locally superlinear convergence are established for this class of algorithms. Finally, we report in details the numerical results which show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2010
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