|
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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.
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BARD, Y. Comparison of gradient methods for the solution of nonlinear parameter estnnation problems SIAM J. Numer. Anal. 7 (1970), 157-186.
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BIGGS, M.C. Minimization algorithms making use of non-quadratic properties of the obJective function. J Inst. Math Appl 8 (1971), 315-327.
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Box, M.J. A comparison of several current optimization methods, and the use of transformations in constrained problems. Comput. J 9 (1966), 67-77.
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BRowN, K.M. A quadratically convergent Newton-hke method based upon Gausslan elmunation. SIAM J. Numer. Anal. 6 (1969), 560-569.
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BROWN, K.M., ASD DENNIS, J.E. New computational algorithms for minimizing a sum of squares of nonhnear functions. Rep. No. 71-6, Yale Univ, Dep. Comput. Science, New Haven, Conn., March 1971.
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BROYDEN, C.G. A class of methods for solving nonlinear simultaneous equations. Math Comput 19 (1965), 577-593.
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BROYDEN, C.G. The convergence of an algorithm for solving sparse nonlinear systems. Math. Comput. 25 (1971), 285-294.
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COLVILLE, A.R. A comparative study of nonlinear programming codes. Rep. 320-2949, IBM New York Scientific Center, 1968.
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Cox, R A. Comparison of the performance of seven optimization algorithms on twelve unconstrained optimization problems. Ref. 1335CNO4, Gulf Research and Development Company, Pittsburg, Jan. 1969.
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FLETCHER, R. Function minimization without evaluating derlvatlves--A review. Comput. J. 8 (1965), 33-41.
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FLETCHER, R., AND POWELL, M.J.D. A rapidly convergent descent method for minn-nizatlon Comput. J. 6 (1963), 163-168.
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GILL, P E, MURRAY W, AND PITFIELD, R.A. The nnplementation of two revised quas~-Newton algorithms for unconstrained optnmzatlon. Rep. NAC 11, National Phys. Lab., April 1972, pp. 82- 83.
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JENNRICH, R.I., AND SAMPSON, P.F. Application of stepwise regression to nonlinear estimation. Technometrtcs 10 (1968), 63-72.
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KOWALIK, J S, AND OSBORNE, M.R Methods for Unconstramed Optimtzatton Problems. Elsevier North-Holland, New York, 1968
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MEYER, R.R. Theoretical and computational aspects of nonlinear regression. In Nonlinear Programmmg, J. B. Rosen, O. L. Mangasanan, and K. Rltter (Eds), Academic Press, New York, 1970, pp. 465-486.
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MOR~, J J. The Levenberg-Marquardt algorithm. Implementation and theory In Numertcal Analysts, G. A. Watson (Ed.), Lecture Notes tn Mathemattcs 630, Sprmger-Verlag, New York, 1977, pp. 105-116.
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OSBORNE, M.R. Some aspects of nonlinear least squares calculations. In Numerical Methods for Nonhnear Optimization, F. A. Lootsma (Ed), Academic Press, New York, 1972, pp 171-189.
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POWELL, M.J.D. A hybrid method for nonlinear equations. In Numer~calMethods for Nonlinear Algebraic Equations, P. Rabinowitz (Ed), Gordon & Breach, New York, 1970, pp. 87-114
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POWELL, M.J.D. An iterative method for finding stationary values of a function of several variables. Comput. J. 5 (1962), 147-151.
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ROSENBROCK, H.H. An automatic method for finding the greatest or least value of a function. Comput. J. 3 (1960), 175-184.
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SPEDICATO, E. Computational experience with quasi-Newton algorithms for minimization problems of moderately large size Rep. CISE-N-175, Segrate (Milano), 1975.
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CITED BY 113
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John J. Tomick , Steven F. Arnold , Russell R. Barton, Sample size selection for improved Nelder-Mead performance, Proceedings of the 27th conference on Winter simulation, p.341-345, December 03-06, 1995, Arlington, Virginia, United States
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P. W. Gaffney , C. A. Addison , B. Andersen , S. Bjørnestad , R. E. England , P. M. Hanson , R. Pickering , M. G. Thomason, NEXUS: towards a problem solving environment (PSE) for scientific computing, ACM SIGNUM Newsletter, v.21 n.3, p.13-24, July 1986
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