Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]

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Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]

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ACM Transactions on Mathematical Software (TOMS) archive
Volume 2 , Issue 1 (March 1976) table of contents

Pages: 87 - 94

Year of Publication: 1976

ISSN:0098-3500

Authors

D. F. Shanno	Computer Science, School of Engineering, University of Mississippi, University, MISS and University of Toronto, Canada
K. H. Phua	Department of Computer Science, Nanyang, University, Upper Jurong Rd., Singapore 22 and University of Toronto, Canada

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 83, Citation Count: 13

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appendices and supplements references cited by index terms collaborative colleagues

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APPENDICES and SUPPLEMENTS

MINI (500.gz) (6 KB)

quasi-Newton: unconstrained minimum of multivariate function
Gams: G1b1b

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	BROYDEN, C.G. The convergence of a class of double-rank minimization algorithms 2. The new algorithm. JIMA 6 (1970), 222-231.
	2	DAVIDON, W.C. Variable metric method for minimization. Rep. ANL-5990, Argonne Nat. Lab., Nov. 1959.
	3	FLETCHER, R. A new approach to variable metric algorithms. Computer J. 13 (1970), 317-322.
	4	FLETCHER, R. A survey of algorithms for unconstrained optimization. Tech. Paper TP 456, Atomic Energy Research Establishment, Harwell, England, june 1971.
	5	GOLDFARB, D. A family of variable-metric methods derived by variational means, Math. Computatwn 2~, (1970), 23-26.
	6	GREENSTAirr;, J. Variations on variable-metric methods. Math. Comp. ~ (1970), 7-18.
	7	POWELL, M.J.D. A fortran subroutine for unconstrained minimization, requiring first derivative of the objective function. Rep. AERE-R 6469, Atomic Energy Research Establishment, Harwell, England, 1970.
	8	POWELL, M.J.D. Recent advances in unconstrained optimization. Math. Programmzng 1 (1971), 26-57.
	9	SHANNO, D.F. Conditioning of quasi-Newton methods for function mimmization. Math. Computation 24 (1970), 647-656.
	10	SHANNO D.F. AND KETTLER, P.C. Optimal conditioning of quasi-Newton methods. Math. Computatwn 24 (1970), 657-664.
	11	SHANNO, D.F., BERG, A., AND CHESTON, G. Restarts and rotations of quasi-Newton methods. Information Processing 74, North-Holland Publ. Co., Amsterdam, 1974, pp. 557-561.
	12	SHANNO, D.F., AND PHUA, K.H. Effective comparison of unconstrained optimization techniques. Manage. Sci. 22 (1975), 321-330.

CITED BY 13

	D. F. Shanno , K. H. Phua, Remark on “Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]”, ACM Transactions on Mathematical Software (TOMS), v.6 n.4, p.618-622, Dec. 1980
	Jorge J. Moré , David J. Thuente, Line search algorithms with guaranteed sufficient decrease, ACM Transactions on Mathematical Software (TOMS), v.20 n.3, p.286-307, Sept. 1994
	Robert B. Schnabel , John E. Koonatz , Barry E. Weiss, A modular system of algorithms for unconstrained minimization, ACM Transactions on Mathematical Software (TOMS), v.11 n.4, p.419-440, Dec. 1985
	A. G. Buckley, Algorithm 734; a Fortran 90 code for unconstrained nonlinear minimization, ACM Transactions on Mathematical Software (TOMS), v.20 n.3, p.354-372, Sept. 1994
	Neculai Andrei, Scaled conjugate gradient algorithms for unconstrained optimization, Computational Optimization and Applications, v.38 n.3, p.401-416, December 2007
	Rudy Setiono, Feedforward Neural Network Construction Using Cross Validation, Neural Computation, v.13 n.12, p.2865-2877, December 2001
	Hongjun Lu , Rudy Setiono , Huan Liu, NeuroRule: A Connectionist Approach to Data Mining, Proceedings of the 21th International Conference on Very Large Data Bases, p.478-489, September 11-15, 1995
	Hongjun Lu , Rudy Setiono, Effective Query Size Estimation Using Neural Networks, Applied Intelligence, v.16 n.3, p.173-183, May-June 2002
	Neculai Andrei, Scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization, Optimization Methods & Software, v.22 n.4, p.561-571, August 2007
	Jochen Wild, Multi-objective constrained optimisation in aerodynamic design of high-lift systems, International Journal of Computational Fluid Dynamics, v.22 n.3, p.153-168, March 2008
	Neculai Andrei, Another nonlinear conjugate gradient algorithm for unconstrained optimization, Optimization Methods & Software, v.24 n.1, p.89-104, February 2009
	A. E. Kostopoulos , T. N. Grapsa, Self-scaled conjugate gradient training algorithms, Neurocomputing, v.72 n.13-15, p.3000-3019, August, 2009
	Neculai Andrei, Accelerated conjugate gradient algorithm with finite difference Hessian/vector product approximation for unconstrained optimization, Journal of Computational and Applied Mathematics, v.230 n.2, p.570-582, August, 2009