Unconstrained derivative-free optimization (NEWUOA)

Thread-safe C++ port of minqa::newuoa(): minimizes a function of many variables by a trust region method that forms quadratic models by interpolation, using M. J. D. Powell's NEWUOA algorithm.

Usage

newuoa(par, fn, control = list(), ...)

Source

M. J. D. Powell's original Fortran 77 NEWUOA code, archived at https://github.com/libprima/prima/tree/main/fortran/original/newuoa; R interface following the minqa package.

Arguments

par

numeric vector of starting parameters.

fn

function to be minimized; its first argument must be the parameter vector and it must return a scalar numeric value.

control

a list of control settings: npt, rhobeg, rhoend, iprint and maxfun, as in minqa::newuoa().

...

further arguments passed to fn.

Value

A list of class c("newuoa", "minqa") with components par, fval, feval, ierr and msg.

References

M. J. D. Powell (2006), "The NEWUOA software for unconstrained optimization without derivatives", in Large-Scale Nonlinear Optimization, Springer, 255-297. doi:10.1007/0-387-30065-1_16

See also

[bobyqa()], [minqa_c_api]

Examples

fr <- function(x) 100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
newuoa(c(1, 2), fr)
#> parameter estimates: 1.00000116212176, 1.00000231682727 
#> objective: 1.35602908489591e-12 
#> number of function evaluations: 136