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fda (version 6.2.0)

lnsrch: Search along a line for a minimum within an optimisation algorithm.

Description

This is a version of the function in Numerical Recipes. It is initialized with a function value and gradient, and it does a series of quadratic searches until a convergence criterion is reached. This version includes code for display the progress of iteration for debugging purposes.

Usage

lnsrch(xold, fold, g, p, func, dataList, stpmax, itermax=20, TOLX=1e-10, dbglev=0)

Value

A named list containing:

x:

The parameter vector at the final minimum.

check:

A boolean value indicating that problems were encountered.

Arguments

xold

The current parameter vector value.

fold

The current function value.

g

The current gradient vector.

p

The current search direction vector.

func

The name of the function being optimized.

dataList

A list object containing objects specifying the function to be minimized.

stpmax

The maximum step size.

itermax

The maximum number of iterations. Default 20

TOLX

Convergence criterion.

dbglev

The level of output. 0: no output; 1: function and slope at each iteration. 2 and above: also results within iteration.

Author

Juan Li and James Ramsay

References

Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

Press, W. H., Taukolsky, S. A., Vetterline, W. T. and Flannery, B. P. (2020) Numerical Recipes, Third Edition, Cambridge.

See Also

smooth.surp