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Line search algorithms with guaranteed sufficient decrease

Published:01 September 1994Publication History
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Abstract

The development of software for minimization problems is often based on a line search method. We consider line search methods that satisfy sufficient decrease and curvature conditions, and formulate the problem of determining a point that satisfies these two conditions in terms of finding a point in a set T(μ). We describe a search algorithm for this problem that produces a sequence of iterates that converge to a point in T(μ) and that, except for pathological cases, terminates in a finite number of steps. Numerical results for an implementation of the search algorithm on a set of test functions show that the algorithm terminates within a small number of iterations.

References

  1. AL-BAALI, M. 1985. Descent property and global convergence of the Fletcher-Reeves method with inexact line searches. IMA J. Namer. Anal. 5, 121-124.Google ScholarGoogle Scholar
  2. AL-BAALI, M., AND FLETCHER, R. 1984. An efficient line search for nonlinear least squares. J. Opttm. Theory Appl. 48, 359-377. Google ScholarGoogle Scholar
  3. BYRD, R. H., NOCEDAL, J., AND YUAN, Y. 1987. Global convergence of a class of quasi-Newton methods on convex problems. SIAM J. Numer. Anal. 24, 1171-1190.Google ScholarGoogle Scholar
  4. DE~, J. E., A~D SCm~AB~L, R. E. 1983. N~rner~cal Method~ for Unconstrained Optimization and Nonhnear Equations. Prentice-Hall, Englewood Cliffs, N.J.Google ScholarGoogle Scholar
  5. FLETCHER, R. 1980. ractzcal Methods of Optzmizat~on. Vol. 1. Unconstrained Optlmization. Wiley, New York Google ScholarGoogle Scholar
  6. FLETCHER, R. 1987. Practwal Methods of Optimization. 2nd ed. Wiley, New York. Google ScholarGoogle Scholar
  7. GILBERT, J. C., AND NOCEDAL, J. 1992. Global convergence properties of conjugate gradient methods for optimization. SIAM J. Optim. 2, 21-42.Google ScholarGoogle Scholar
  8. G~LL, P. E., ANn MURKY, W. 1974. Safeguarded steplength algorithms for optimization using descent methods. Rep. NAC 37, National Physical Laboratory, Teddington, England.Google ScholarGoogle Scholar
  9. GILL, P. E., MURRAY, W., SAUNVERS, M. A., AND WRIGHT, M. H. 1979. Two steplength algorithms for numerical optimization. Rep. SOL 79-25, Systems Optimization Laboratory, Stanford Univ., Stanford, Calif.Google ScholarGoogle Scholar
  10. HAGER, W. W. 1989. A derivative-based bracketing scheme for univariate minimization and the conjugate gradient method. Comput. Math. Appl. 18, 779-795.Google ScholarGoogle Scholar
  11. LEMARECnAL, C. 1981. A view of line-searches. In Optimization and Optimal Control, A. Auslender, W. Oettli, and J. Stoer, Eds. Lecture Notes in Control and Information Science, vol. 30. Springer-Verlag, Heidelberg, 59 78.Google ScholarGoogle Scholar
  12. LIu, D. C., ~3~D NOCEDAL, J. 1989. On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503-528. Google ScholarGoogle Scholar
  13. MOR~, J. J., AND SORENSEN, D. C. 1984. Newton's method. In Studies in Numerical Analysis, G. H. Golub, Ed. Mathematical Association of America, Washington, D.C., 29 82.Google ScholarGoogle Scholar
  14. O'LEARY, D. P. 1990. Robust regression computation using iteratively reweighted least squares. SIAM J. Matrix Anal. Appl. 11,466-480. Google ScholarGoogle Scholar
  15. POWELL, M. J. D. 1976. Some global convergence properties of a variable metric method without line searches. In Nonlinear Programming, R. W. Cottie and C. E. Lemke, Eds. SIAM-AMS Proceedings, vol. IX. American Mathematical Society, Providence, R.I., 53-72.Google ScholarGoogle Scholar
  16. SCHLICK, T., AND FOGELSON, A. 1992a. TNPACK--A truncated Newton minimization package for large-scale problems: I. Algorithms and usage. ACM Trans. Math. Softw. 18, 1, 46-70. Google ScholarGoogle Scholar
  17. SC~L~CK, T., ANn FOGELSON, A. 1992b. TNPACK--A truncated Newton minimization package for large-scale problems: II. Implementations examples. ACM Trans. Math. Softw. 18, 1, 71-111. Google ScholarGoogle Scholar
  18. SHANNO, D. F., ^ND PHUA, K. H. 1976. Minimization of unconstrained multivariate functions. ACM Trans. Math. Softw. 2, 87 94. Google ScholarGoogle Scholar
  19. SH~NO, D. F., ~D PHVA, K. H. 1980. Remark on Algorithm 500. ACM Trans. Math. Softw. 6, 618-622.Google ScholarGoogle Scholar
  20. YANAL H., OZAWA, M., AND KANEKO, S. 1981. Interpolation methods in one dimensional optimization. Computing 27, 155-163.Google ScholarGoogle Scholar

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