An easy-to-use modified Gauss-Newton algorithm for finding an unconstrained minimum of a sum of squares of M nonlinear functions in N variables (m .ge. n). First derivatives are required. Classes : K1b1a2 . Unconstrained nonlinear least squares approximation by smooth functions, user provides first derivatives L8e1b2 . Parameter estimation in nonlinear least squares regression using unweighted data, user provides derivatives Type : Fortran subroutine in NAG library (E04 sublibrary). Access : Proprietary. Many implementations available. Precision: Double. Usage : CALL E04GEF (M, N, X, FSUMSQ, IW, LIW, W, LW, IFAIL) See also : E04YCF Details : Documentation Example Example-input Example-output Sites : (1) ITL
ITL: Unix Workstation Network, National Institute of Standards and Technology (NIST), Gaithersburg, MD. Available to NIST staff. Precision: Double. Access available only to NIST staff on internal Unix systems. They may access this package provided the /itl tree is cross-mounted. Link : f77 -o prog prog.f {-lcomplib.sgimath} -L/itl/links/generic/{lib lib32 lib64}{/mips3 /mips4} -lnag Documentation: acroread /itl/apps/naglib-19/flib619da/NAGdoc/fl/pdf/E04/e04_intro_fl19.pdf Example : cat /itl/apps/naglib-18/fllux18d9/examples/source/e04gefe.f Example-input: cat /itl/apps/naglib-18/fllux18d9/examples/data/e04gefe.d Example-outpu: cat /itl/apps/naglib-18/fllux18d9/examples/results/e04gefe.r
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