Minimize Unconstrained Multivariable Function
Find minimum of unconstrained multivariable functions.
fminunc(x0, fn, gr = NULL, ...,
tol = 1e-08, maxiter = 0, maxfeval = 0)x0 |
starting point. |
fn |
objective function to be minimized. |
gr |
gradient function of the objective. |
... |
additional parameters to be passed to the function. |
tol |
relative tolerance. |
maxiter |
maximum number of iterations. |
maxfeval |
maximum number of function evaluations. |
The method used here for unconstrained minimization is a variant of a "variable metric" resp. quasi-Newton approach.
List with the following components:
par |
the best minimum found. |
value |
function value at the minimum. |
counts |
number of function and gradient calls. |
convergence |
integer indicating the terminating situation. |
message |
description of the final situation. |
fminunc mimics the Matlab function of the same name.
The "variable metric" code provided by John Nash (package Rvmmin), stripped-down version by Hans W. Borchers.
J. Nocedal and S. J. Wright (2006). Numerical Optimization. Second Edition, Springer Science+Business Media, New York.
fun = function(x)
x[1]*exp(-(x[1]^2 + x[2]^2)) + (x[1]^2 + x[2]^2)/20
fminunc(x0 = c(1, 2), fun)
## xmin: c(-0.6691, 0.0000); fmin: -0.4052Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.