New Unconstrained Optimization with quadratic Approximation
NEWUOA solves quadratic subproblems in a spherical trust regionvia a truncated conjugate-gradient algorithm. For bound-constrained problems, BOBYQA shold be used instead, as Powell developed it as an enhancement thereof for bound constraints.
newuoa(x0, fn, nl.info = FALSE, control = list(), ...)
x0 |
starting point for searching the optimum. |
fn |
objective function that is to be minimized. |
nl.info |
logical; shall the original NLopt info been shown. |
control |
list of options, see |
... |
additional arguments passed to the function. |
This is an algorithm derived from the NEWUOA Fortran subroutine of Powell, converted to C and modified for the NLOPT stopping criteria.
List with components:
par |
the optimal solution found so far. |
value |
the function value corresponding to |
iter |
number of (outer) iterations, see |
convergence |
integer code indicating successful completion (> 0) or a possible error number (< 0). |
message |
character string produced by NLopt and giving additional information. |
NEWUOA may be largely superseded by BOBYQA.
Hans W. Borchers
M. J. D. Powell. “The BOBYQA algorithm for bound constrained optimization without derivatives,” Department of Applied Mathematics and Theoretical Physics, Cambridge England, technical reportNA2009/06 (2009).
fr <- function(x) { ## Rosenbrock Banana function
100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
}
(S <- newuoa(c(1, 2), fr))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.