|
|
|
Nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization |
Lijuan ZHAO1,2,Wenyu SUN1,Raimundo J. B. de SAMPAIO3,*( ) |
1. School of Mathematical Sciences, Jiangsu Key Laboratory for NSLSCS, Nanjing Normal University, Nanjing 210023, China 2. Department of Social Science Teaching, Nanjing Institute of Railway Technology, Nanjing 210031, China 3. PPGEPS, Pontifical Catholic University of Parana (PUCPR), Curitiba, Parana, Brazil |
|
|
|
|
Abstract We propose a nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization. Unlike traditional trust region methods, the subproblem in our method is a simple conic model, where the Hessian of the objective function is approximated by a scalar matrix. The trust region radius is adjusted with a new self-adaptive adjustment strategy which makes use of the information of the previous iteration and current iteration. The new method needs less memory and computational efforts. The global convergence and Q-superlinear convergence of the algorithm are established under the mild conditions. Numerical results on a series of standard test problems are reported to show that the new method is effective and attractive for large scale unconstrained optimization problems.
|
| Keywords
Nonmonotone technique
conic model
trust region method
large scale optimization
global convergence
|
|
Corresponding Author(s):
Raimundo J. B. de SAMPAIO
|
|
Issue Date: 26 August 2014
|
|
| 1 |
Andrei N. Scaled conjugate gradient algorithms for unconstrained optimization. Comput Optim Appl, 2007, 38: 401-416 doi: 10.1007/s10589-007-9055-7
|
| 2 |
Andrei N. An unconstrained optimization test functions collection. Adv Model Optim, 2008, 10: 147-161
|
| 3 |
Ariyawansa K A. Deriving collinear scaling algorithms as extensions of quasi-Newton methods and the local convergence of DFP and BFGS related collinear scaling algorithm. Math Program, 1990, 49: 23-48 doi: 10.1007/BF01588777
|
| 4 |
Ariyawansa K A, Lau D T M. Local and Q-superlinear convergence of a class of collinear scaling algorithms that extends quasi-Newton methods with Broyden’s bounded-? class of updates. Optimization, 1992, 23: 323-339 doi: 10.1080/02331939208843768
|
| 5 |
Conn A R, Gould N I M, Toint Ph L. Trust-Region Methods. Philadelphia: SIAM, 2000 doi: 10.1137/1.9780898719857
|
| 6 |
Cui Z C, Wu B Y, Qu S J. Combining nonmonotone conic trust region and line search techniques for unconstrained optimization. J Comput Appl Math, 2011, 235: 2432-2441 doi: 10.1016/j.cam.2010.10.044
|
| 7 |
Dai Y H. A nonmonotone conjugate gradient algorithm for unconstrained optimization. J Syst Sci Complex, 2002, 15: 139-145
|
| 8 |
Dai Y H. On the nonmonotone line search. J Optim Theory Appl, 2002, 112: 315-330 doi: 10.1023/A:1013653923062
|
| 9 |
Davidon W C. Conic approximations and collinear scalings for optimizers. SIAM J Numer Anal, 1980, 17: 268-281 doi: 10.1137/0717023
|
| 10 |
Deng N Y, Li Z F. Some global convergence properties of a conic variable metric algorithm for minimization with inexact line search. Optim Methods Softw, 1995, 5: 105-122 doi: 10.1080/10556789508805604
|
| 11 |
Di S, Sun W. A trust region method for conic model to solve unconstrained optimizations. Optim Methods Softw, 1996, 6: 237-263 doi: 10.1080/10556789608805637
|
| 12 |
Dolan E D, Moré J J. Benchmarking optimization software performance profiles. Math Program, 2002, 91: 201-213 doi: 10.1007/s101070100263
|
| 13 |
Fu J H, Sun W, de Sampaio R J B. An adaptive approach of conic trust region method for unconstrained optimization problems. J Appl Math Comput, 2005, 19: 165-177 doi: 10.1007/BF02935796
|
| 14 |
Gourgeon H, Nocedal J. A conic algorithm for optimization. SIAM J Sci Stat Comput, 1985, 6: 253-267
|
| 15 |
Grippo L, Lampariello F, Lucidi S. A nonmonotone line search technique for Newton’s method. SIAM J Numer Anal, 1986, 23: 707-716 doi: 10.1137/0723046
|
| 16 |
Grippo L, Sciandrone M. Nonmonotone globalization techniques for the Barzilai-Borwein gradient method. Comput Optim Appl, 2002, 23: 143-169 doi: 10.1023/A:1020587701058
|
| 17 |
Han Q, Sun W, Han J, Sampaio R J B. An adaptive trust-region method for unconstrained optimization. Optim Methods Softw, 2005, 20: 665-677 doi: 10.1080/10556780410001697677
|
| 18 |
Ji Y, Li Y J, Zhang K C, Zhang X L. A new nonmonotone trust-region method of conic model for solving unconstrained optimization. J Comput Appl Math, 2010, 233: 1746-1754 doi: 10.1016/j.cam.2009.09.011
|
| 19 |
Moré J J. Recent developments in algorithms and software for trust region methods. In: Bachem A, Gr?tschel M, Korte B, eds. Mathematical Programming: The State of the Art. Berlin: Springer, 1983, 258-287
|
| 20 |
Ni Q. Optimality conditions for trust region subproblems involving a conic model. SIAM J Optim, 2005, 15: 828-837 doi: 10.1137/S1052623402418991
|
| 21 |
Nocedal J, Wright S J. Numerical Optimization. New York: Springer, 1999 doi: 10.1007/b98874
|
| 22 |
Powell M J D. A new algorithm for unconstrained optimization. In: Rosen J B, Mangasarian O L, Ritter K, eds. Nonlinear Programming. New York: Academic Press, 1970, 31-65 doi: 10.1016/B978-0-12-597050-1.50006-3
|
| 23 |
Powell M J D, Toint Ph L. On the estimation of sparse Hessian matrices. SIAM J Numer Anal, 1979, 16: 1060-1074 doi: 10.1137/0716078
|
| 24 |
Powell M J D, Yuan Y. A trust region algorithm for equality constrained optimization. Math Program, 1991, 49: 189-211 doi: 10.1007/BF01588787
|
| 25 |
Sang Z Y, Sun Q Y. A self-adaptive trust region method with line search based on a simple subproblem model. J Comput Appl Math, 2009, 232: 514-522 doi: 10.1016/j.cam.2009.06.027
|
| 26 |
Schubert L K. Modification of a quasi-Newton method for nonlinear equations with sparse Jacobian. Math Comp, 1970, 24: 27-30 doi: 10.1090/S0025-5718-1970-0258276-9
|
| 27 |
Sorensen D C. The Q-superlinear convergence of a collinear scaling algorithm for unconstrained optimization. SIAM J Numer Anal, 1980, 17: 84-114 doi: 10.1137/0717011
|
| 28 |
Sun Q Y, Duan L N, Cui B. A nonmonotone trust region algorithm with simple quadratic models. J Systems Sci Math Sci, 2009, 29: 470-483
|
| 29 |
Sun W. Optimization methods for non-quadratic model. Asia-Pac J Oper Res, 1996, 13: 43-63
|
| 30 |
Sun W. Nonmonotone trust region method for solving optimization problems. Appl Math Comput, 2004, 156: 159-174 doi: 10.1016/j.amc.2003.07.008
|
| 31 |
Sun W, Han J, Sun J. Global convergence of nonmonotone descent methods for unconstrained optimization problems. J Comput Appl Math, 2002, 146: 89-98 doi: 10.1016/S0377-0427(02)00420-X
|
| 32 |
Sun W, Xu D. A filter-trust-region method based on conic model for unconstrained optimization. Sci China Math, 2012, 42: 527-543 (in Chinese)
|
| 33 |
Sun W, Yuan Y. Optimization Theory and Methods: Nonlinear Programming. New York: Springer, 2006
|
| 34 |
Toint Ph L. Towards an efficient sparsity exploiting Newton method for minimization. In: Duff I S, ed. Sparse Matrices and Their Uses. New York: Academic Press, 1981, 57-88
|
| 35 |
Toint Ph L. An assessment of non-monotone line search techniques for unconstrained optimization. SIAM J Sci Comput, 1996, 17: 725-739 doi: 10.1137/S106482759427021X
|
| 36 |
Xu C X, Yang X Y. Convergence of conic-quasi-Newton trust-region methods for unconstrained optimization. Math Appl (Wuhan), 1998, (2): 71-76 (in Chinese)
|
| 37 |
Zhang H C, Hager W W. A nonmonotone line search technique and its application to unconstrained optimization. SIAM J Optim, 2004, 14: 1043-1056 doi: 10.1137/S1052623403428208
|
| 38 |
Zhou Q Y, Zhang C. A new nonmonotone adaptive trust region method based on simple quadratic models. J Appl Math Comput, 2012, 40: 111-123 doi: 10.1007/s12190-012-0572-x
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|