fminunc: Minimize Unconstrained Multivariable Function

In pracma: Practical Numerical Math Functions

Minimize Unconstrained Multivariable Function

Description

Find minimum of unconstrained multivariable functions.

Usage

  fminunc(x0, fn, gr = NULL, ...,
          tol = 1e-08, maxiter = 0, maxfeval = 0)

Arguments

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.

Details

The method used here for unconstrained minimization is a variant of a "variable metric" resp. quasi-Newton approach.

Value

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.

Note

fminunc mimics the Matlab function of the same name.

Author(s)

The "variable metric" code provided by John Nash (package Rvmmin), stripped-down version by Hans W. Borchers.

References

J. Nocedal and S. J. Wright (2006). Numerical Optimization. Second Edition, Springer Science+Business Media, New York.

See Also

fminsearch, fmincon,

Examples

  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: -0.4052

pracma documentation built on March 19, 2024, 3:05 a.m.