2013, 3(2): 223-234. doi: 10.3934/naco.2013.3.223

Subspace trust-region algorithm with conic model for unconstrained optimization

1. 

Jinling College of Nanjing University, Nanjing 210089, China

2. 

Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received  December 2011 Revised  January 2013 Published  April 2013

In this paper, a new subspace algorithm is proposed for unconstrained optimization. In this new algorithm, the subspace technique is used in the trust region subproblem with conic model, and the dogleg method is modified to solve this subproblem. The global convergence of this algorithm under some reasonable conditions is established. Numerical experiment shows that this algorithm may be superior to the corresponding algorithm without using subspace technique especially for large scale problems.
Citation: Xin Zhang, Jie Wen, Qin Ni. Subspace trust-region algorithm with conic model for unconstrained optimization. Numerical Algebra, Control & Optimization, 2013, 3 (2) : 223-234. doi: 10.3934/naco.2013.3.223
References:
[1]

W. C. Davidon, Conic approximation and collinear scaling for optimizers,, SIAM J.Number. Anal., 17 (1980), 268.  doi: 10.1137/0717023.  Google Scholar

[2]

S. Di and W. Y. Sun, A trust region Method for conic model to solve unconstrained optimization,, Optimization Methods and Software, 6 (1996), 237.  doi: 10.1080/10556789608805637.  Google Scholar

[3]

S. D. Jiang, "A Quasi-Newton Trust Region Method with a New Conic Model for the Unconstrained Optimization,", S. M thesis, (2005).   Google Scholar

[4]

X. P. Lu, Q. Ni and H. Liu, A dogleg method for solving new trust region subproblems of conic model,, Acta Math. Appl. Sin (in Chinese), 30 (2007), 855.   Google Scholar

[5]

X. P. Lu and Q. Ni, A quasi-Newton trust region method with a new conic model for the unconstrained optimization,, Applied Mathematics and Computation, 204 (2008), 373.  doi: 10.1016/j.amc.2008.06.062.  Google Scholar

[6]

J. J. More, B. S. Garbow and K. E. Hillstrom, Testing unconstrained optimization software,, ACM Trans. Math. Software, 7 (1981), 17.  doi: 10.1145/355934.355936.  Google Scholar

[7]

Q. Ni, Optimality conditions for trust-region subproblems involving a conic model,, SIAM J. Optimization, 15 (2005), 826.  doi: 10.1137/S1052623402418991.  Google Scholar

[8]

M. J. D. Powell, A new algorithm for unconstrained optimization,, in, (1970), 31.   Google Scholar

[9]

M. J. D. Powell, A hybrid method for nonlinear equations,, in, (1970), 87.   Google Scholar

[10]

M. J. D. Powell, Convergence properties of a class of minimization algorithms,, in, (1974), 1.   Google Scholar

[11]

M. J. D. Powell and Y. X. Yuan, A trust region algorithm for equality constrained optimization,, Math. Program., 49 (1991), 189.  doi: 10.1007/BF01588787.  Google Scholar

[12]

Y. X. Yuan, A review of trust region algorithms for optimization,, in, (2000), 271.   Google Scholar

[13]

H. W. Zhou and Y. X. Yuan, A subspace implementation of quasi-newton trust region methods for unconstrained optimization,, Report, (2004).   Google Scholar

[14]

M. F. Zhu, Y. Xue and F. S. Zhang, A quasi-Newton type trust region method based on the conic model,, Numer. Math. (A Journal of Chinese Universities), 17 (1995), 36.   Google Scholar

show all references

References:
[1]

W. C. Davidon, Conic approximation and collinear scaling for optimizers,, SIAM J.Number. Anal., 17 (1980), 268.  doi: 10.1137/0717023.  Google Scholar

[2]

S. Di and W. Y. Sun, A trust region Method for conic model to solve unconstrained optimization,, Optimization Methods and Software, 6 (1996), 237.  doi: 10.1080/10556789608805637.  Google Scholar

[3]

S. D. Jiang, "A Quasi-Newton Trust Region Method with a New Conic Model for the Unconstrained Optimization,", S. M thesis, (2005).   Google Scholar

[4]

X. P. Lu, Q. Ni and H. Liu, A dogleg method for solving new trust region subproblems of conic model,, Acta Math. Appl. Sin (in Chinese), 30 (2007), 855.   Google Scholar

[5]

X. P. Lu and Q. Ni, A quasi-Newton trust region method with a new conic model for the unconstrained optimization,, Applied Mathematics and Computation, 204 (2008), 373.  doi: 10.1016/j.amc.2008.06.062.  Google Scholar

[6]

J. J. More, B. S. Garbow and K. E. Hillstrom, Testing unconstrained optimization software,, ACM Trans. Math. Software, 7 (1981), 17.  doi: 10.1145/355934.355936.  Google Scholar

[7]

Q. Ni, Optimality conditions for trust-region subproblems involving a conic model,, SIAM J. Optimization, 15 (2005), 826.  doi: 10.1137/S1052623402418991.  Google Scholar

[8]

M. J. D. Powell, A new algorithm for unconstrained optimization,, in, (1970), 31.   Google Scholar

[9]

M. J. D. Powell, A hybrid method for nonlinear equations,, in, (1970), 87.   Google Scholar

[10]

M. J. D. Powell, Convergence properties of a class of minimization algorithms,, in, (1974), 1.   Google Scholar

[11]

M. J. D. Powell and Y. X. Yuan, A trust region algorithm for equality constrained optimization,, Math. Program., 49 (1991), 189.  doi: 10.1007/BF01588787.  Google Scholar

[12]

Y. X. Yuan, A review of trust region algorithms for optimization,, in, (2000), 271.   Google Scholar

[13]

H. W. Zhou and Y. X. Yuan, A subspace implementation of quasi-newton trust region methods for unconstrained optimization,, Report, (2004).   Google Scholar

[14]

M. F. Zhu, Y. Xue and F. S. Zhang, A quasi-Newton type trust region method based on the conic model,, Numer. Math. (A Journal of Chinese Universities), 17 (1995), 36.   Google Scholar

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