| newuoa {nloptr} | R Documentation |
New Unconstrained Optimization with quadratic Approximation
Description
NEWUOA solves quadratic subproblems in a spherical trust region via a truncated conjugate-gradient algorithm. For bound-constrained problems, BOBYQA should be used instead, as Powell developed it as an enhancement thereof for bound constraints.
Usage
newuoa(x0, fn, nl.info = FALSE, control = list(), ...)
Arguments
x0 |
starting point for searching the optimum. |
fn |
objective function that is to be minimized. |
nl.info |
logical; shall the original NLopt info be shown. |
control |
list of options, see |
... |
additional arguments passed to the function. |
Details
This is an algorithm derived from the NEWUOA Fortran subroutine of Powell, converted to C and modified for the NLopt stopping criteria.
Value
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. |
Note
NEWUOA may be largely superseded by BOBYQA.
Author(s)
Hans W. Borchers
References
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).
See Also
Examples
## Rosenbrock Banana function
rbf <- function(x) {(1 - x[1]) ^ 2 + 100 * (x[2] - x[1] ^ 2) ^ 2}
S <- newuoa(c(1, 2), rbf)
## The function as written above has a minimum of 0 at (1, 1)
S