Jorge J. Moré
- 1 BARD, Y. Comparison of gradient methods for the solution of nonlinear parameter estnnation problems SIAM J. Numer. Anal. 7 (1970), 157-186.Google Scholar
- 2 BEALE, E.M.L. On an iterative method of finding a local mmnnum of a function of more than one variable. Tech. Rep. No. 25, Statistical Techniques Research Group, Princeton Umv., Princeton, N.J., 1958.Google Scholar
- 3 BIGGS, M.C. Minimization algorithms making use of non-quadratic properties of the obJective function. J Inst. Math Appl 8 (1971), 315-327.Google Scholar
- 4 Box, M.J. A comparison of several current optimization methods, and the use of transformations in constrained problems. Comput. J 9 (1966), 67-77.Google Scholar
- 5 BRowN, K.M. A quadratically convergent Newton-hke method based upon Gausslan elmunation. SIAM J. Numer. Anal. 6 (1969), 560-569.Google Scholar
- 6 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.Google Scholar
- 7 BROYDEN, C.G. A class of methods for solving nonlinear simultaneous equations. Math Comput 19 (1965), 577-593.Google Scholar
- 8 BROYDEN, C.G. The convergence of an algorithm for solving sparse nonlinear systems. Math. Comput. 25 (1971), 285-294.Google Scholar
- 9 COLVILLE, A.R. A comparative study of nonlinear programming codes. Rep. 320-2949, IBM New York Scientific Center, 1968.Google Scholar
- 10 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.Google Scholar
- 11 FLETCHER, R. Function minimization without evaluating derlvatlves--A review. Comput. J. 8 (1965), 33-41.Google Scholar
- 12 FLETCHER, R., AND POWELL, M.J.D. A rapidly convergent descent method for minn-nizatlon Comput. J. 6 (1963), 163-168.Google Scholar
- 13 FREUDENSTEIN, F., AND ROTH, B. Numerical solutions of systems of nonlinear equations. J ACM 10, 4 (Oct. 1963), 550-556. Google Scholar
- 14 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.Google Scholar
- 15 HILLSTROM, K.E. A snnulaUon test approach to the evaluation of nonhnear opt~rmzatlon algorithms. A CM Trans. Math. Softw. 3, 4 (1977), 305-315. Google Scholar
- 16 JENNRICH, R.I., AND SAMPSON, P.F. Application of stepwise regression to nonlinear estimation. Technometrtcs 10 (1968), 63-72.Google Scholar
- 17 KOWALIK, J S, AND OSBORNE, M.R Methods for Unconstramed Optimtzatton Problems. Elsevier North-Holland, New York, 1968Google Scholar
- 18 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.Google Scholar
- 19 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.Google Scholar
- 20 MOR}~, J.J., AND COSNARD, M.Y. Numerical solution of nonlinear equations. ACM Trans. Math. Softw 5, 1 (March 1979), 64-85. Google Scholar
- 21 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.Google Scholar
- 22 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. 87-114Google Scholar
- 23 POWELL, M.J.D. An iterative method for finding stationary values of a function of several variables. Comput. J. 5 (1962), 147-151.Google Scholar
- 24 ROSENBROCK, H.H. An automatic method for finding the greatest or least value of a function. Comput. J. 3 (1960), 175-184.Google Scholar
- 25 SPEDICATO, E. Computational experience with quasi-Newton algorithms for minimization problems of moderately large size Rep. CISE-N-175, Segrate (Milano), 1975.Google Scholar
Index Terms
- Testing Unconstrained Optimization Software
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