stabilNewton {pcme} | R Documentation |
Implements a hybrid method of minimisation based on gradient and Newton methods.
stabilNewton(f, x, alpha = 10^(-4), beta = 0.9, xvaltol = 10^(-6), gradtol = 10^{-12}, gradnormtol = 10^{-8}, itermax = 100, fonly, choosedirtol = 10^(-12), betaflag = TRUE)
f |
evaluate the function, its gradient, and Hessian, see Details. |
x |
initial value. |
alpha |
damping constant for Newton direction. |
beta |
modifier for further damping in Newton direction. |
xvaltol |
tollerance for stopping rules. |
gradtol |
tollerance for stopping rules. |
gradnormtol |
tollerance for stopping rules. |
itermax |
limit for number of iterations. |
fonly |
evaluates f, but not derivatives. |
choosedirtol |
??? |
betaflag |
??? |
A list with the following components:
res$estimate |
the value of the argument where the minimum occurs. |
res$karmijo |
??? |
res$iterations |
number of iterations. |
res$minimum |
f(x), the value of the function at res$estimate . |
res$gradient |
gradient of f at res$estimate . |
res$hessian |
Hessian of f at res$estimate . |
res$cntgrad |
number of computations of the gradient. |
res$cnthes |
number of computations of the Hessian. |
res$optimhistory |
a log of the optimisation steps. |
To do: document this function and the method properly and add references.
Georgi Boshnakov