A class of trust-region methods for parallel optimization

SIAM Journal on Optimization

Volumn 13, Issue 1, 2003, Pages 264-282

  Hough, P D ^a   Meza, J C ^a  

^a SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

Direct search methods; Nonlinear programming; Parallel optimization; Trust region methods

Indexed keywords

EID: 0037289521     PISSN: 10526234     EISSN: None     Source Type: Journal    
DOI: 10.1137/S1052623498343799     Document Type: Article

References (22)

1. N. ALEXANDROV, J. E. DENNIS, JR., R. M. LEWIS, AND V. TORCZON, A trust region framework for managing the use of approximation models in optimization, Structural Optim., 15 (1998), pp. 16-12.
2. R. H. BYRD, R. B. SCHNABEL, AND G. A. SHULTZ, Parallel quasi-Newton methods for unconstrained optimization, Math. Programming, 42 (1988), pp. 273-306.
3. R. G. CARTER, On the global convergence of trust region algorithms using inexact gradient information, SIAM J. Numer. Anal., 28 (1991), pp. 251-265.
4. R. G. CARTER, Numerical experience with a class of algorithms for nonlinear optimization using inexact function and gradient information, SIAM J. Sci. Comput., 14 (1993), pp. 368-388.
5. A. R. CONN, N. I. M. GOULD, AND P. L. TOINT, Testing a class of methods for solving minimization problems with simple bounds on the variables, Math. Comp., 50 (1988), pp. 399-430.
6. J. E. DENNIS, JR., AND R. B. SCHNABEL, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.
7. J. E. DENNIS, JR., AND V. TORCZON, Direct search methods on parallel machines, SIAM J. Optim., 1 (1991), pp. 448-474.
8. P. E. GILL, W. MURRAY, AND M. H. WRIGHT, Practical Optimization, Academic Press, San Diego, CA, 1981.
9. J. F. GRCAR, TWOPNT Program for Boundary Value Problems, Version 3.10, Tech. report SAND91-8230, Sandia National Laboratory, Livermore, CA, 1992.
10. W. G. HOUF, J. F. GRCAR, AND W. G. BREILAND, A model for low pressure chemical vapor deposition in a hot-wall tubular reactor, Materials Science Engineering B, Solid State Materials for Advanced Technology, 17 (1993), pp. 163-171.
11. P. D. HOUGH AND J. C. MEZA, A Class of Trust-Region Methods for Parallel Optimization, Tech. report SAND98-8245, Sandia National Laboratories, Livermore, CA, 1999.
12. V. E. HOWLE, S. M. SHONTZ, AND P. D. HOUGH, Some Parallel Extensions to Optimization Methods in OPT++, Tech. report SAND2000-8877, Sandia National Laboratories, Livermore, CA, 2000.
13. L. INGBER, Simulated annealing: Practice versus theory, Math. Comput. Modelling, 18 (1993), pp. 29-57.
14. P. JOG, J. Y. SUH, AND D. VAN GUCHT, Parallel genetic algorithms applied to the traveling salesman problem, SIAM J. Optim., 1 (1991), pp. 515-529.
15. C. T. KELLEY, A Shamanskii-like acceleration scheme for nonlinear equations at singular roots, Math. Comp., 47 (1986), pp. 609-623.
16. P. J. M. LAARHOVEN, Parallel variable metric algorithms for unconstrained optimization, Math. Programming, 33 (1985), pp. 68-81.
17. C. D. MOEN, P. A. SPENCE, AND J. C. MEZA, Automatic differentiation for gradient-based optimization of radiatively heated microelectronics manufacturing equipment, in Proceedings of the 6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, 1996, pp. 1167-1175.
18. J. J. MORÉ, B. S. GARBOW, AND K. E. HILLSTROM, Testing unconstrained optimization software, ACM Trans. Math. Software, 7 (1981), pp. 17-41.
19. P. K. PHUA AND Y. ZENG, Parallel Quasi-Newton Algorithms for Large-Scale Optimization, Tech. report TRB2/95, National University of Singapore, Singapore, 1995.
20. L. B. RALL, Convergence of the Newton process to multiple solutions, Numer. Math., 9 (1966), pp. 23-37.
21. R. B. SCHNABEL, A view of the limitations, opportunities, and challenges in parallel nonlinear optimization, Parallel Comput., 21 (1995), pp. 875-905.
22. T. A. STRAETER, A Parallel Variable Metric Optimization Algorithm, Tech. report NASA TN D-7329, NASA, Langley Research Center, Hampton, VA, 1973.