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Package levmar (in levmar.i) -
Index of documented functions or symbols:
DOCUMENT a = levmar(y, x, f, a0, avar, acovar)
perform a Levenberg-Marquardt non-linear least squares fit
to data values Y, which are functions of X. The dimensions
of X need bear no particular relationship to the dimensions
of Y, but Y must be a 1D array. The function F maps X into Y,
as a function of some additional parameters A, according to
Y = F(X, A)
Again, the dimensions of A bear no particular relation to
the dimensions of either X or Y. The input A0 is the initial
estimate of the parameter values, which must be made with some
care for the algorithm to converge. Note that it may not converge
to the expected relative minimum in many situations.
The parameters Y and A must be 1D arrays, X can be anything.
AVAR and ACOVAR are return arguments; ACOVAR is the covariance
matrix of the fit parameters A, and AVAR is its diagonal. Hence
AVAR has the same dimensions as A0, while ACOVAR is a 2D symmetric
matrix, whose diagonal is AVAR. Multiply AVAR or ACOVAR by
levmar_chi2 if you did not use wgt= and want to use the quality
of the fit to estimate the variances of the parameters.
ACOVAR is what is returned by regress_cov, see help,regress_cov.
You may be able to improve the performance of levmar by supplying
analytic derivatives. To support this improvement, levmar permits
three different "prototypes" for the function F:
func F(x, a)
if you cannot compute analytic dfda
approximate dfda will be computed by levmar_partial (see help)
func F(x, a, &dfda)
if you can return analytic dfda
if levmar will use dfda, dfda=1 on input
if levmar will not use dfda, dfda=[] on input
func F(x, a, &dfda, deriv=)
if levmar will use dfda, it sets deriv=1, else deriv=0
(this form is for backward compatibility with original lmfit)
The levmar function automatically detects the prototype of F. (!!)
In all cases, dfda is defined as:
dfda(i,j) = partial[Y(i)] / partial[A(j)]
Note that the external variable fit (the fit= keyword to levmar)
is available to F, indicating that only a subset of the partial
derivatives must be computed. Set unused j indices to zero.
EXTERNAL VARIABLES:
outputs:
levmar_chi2 final value of chi2
levmar_chi20 initial value of chi2 (for a0)
levmar_lambda final value of lambda
levmar_neval number of calls to F
inputs:
levmar_itmax = 100 maximum number of gradient recalculations
levmar_tol = 1.e-7 stop when chi2 changes by less than this
levmar_lambda0 = 0.001 initial value of lambda
levmar_lambda1 = 1.e12 maximum permitted value of lambda
levmar_gain = 10. factor by which to change lambda
levmar_aabs, levmar_arel, levmar_ada -- see levmar_partial
KEYWORDS:
fit= index list into a0 of parameters to be varied
the returned model will equal a0 for parameters not varied,
avar and acovar will be 0.0 for parameters not varied
amin= minimum values for parameters, same size as a0
amax= maximum parameter values, same size as a0
the function F will not be called with parameters outside
these specified ranges
wgt= same size as Y, weightings for each point
if sigma_y(i) is standard deviation of i-th point, then
wgt=1./sigma_y^2 is the appropriate weight.
lu= set non-zero to use LUsolve instead of SVsolve
LUsolve will be faster, which will only be an issue if the
number of parameters is large
SEE ALSO: regress, levmar_partial
DOCUMENT y = levmar_partial(f, x, a, dfda)
return y=F(X,A) and DFDA(i,j) = partial[F(X,A)(i)] / partial[A(j)]
by finite differences. Accepts fit=, amin=, amax= keywords with
same meaning as levmar. DFDA is only computed if DFDA=1 on input;
if DFDA=[] or 0, the (expensive) DFDA calculation is skipped.
Uses levmar_arel and levmar_aabs to compute the step sizes
use to compute the patial derivatives as follows:
da = levmar_aabs + levmar_arel*abs(A)
(Hence levmar_aabs, levmar_arel can be arrays with the same length
as the parameter array A.) By default, levmar_arel = 1.e-6, and
levmar_aabs = 0. If levmar_aabs = 0 and A(i)=0, then da(i)=1.e-9.
You can also supply a function, levmar_ada(A) returning da>0 by
whatever formula you wish.
SEE ALSO: levmar