newuoa

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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.

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 been shown.

control

list of options, see nl.opts for help.

...

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 par.

iter

number of (outer) iterations, see maxeval.

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NEWUOA may be largely superseded by BOBYQA.

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

bobyqa, cobyla

Aliases
  • newuoa
Examples
# NOT RUN {
fr <- function(x) {   ## Rosenbrock Banana function
    100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
}
(S <- newuoa(c(1, 2), fr))

# }
Documentation reproduced from package nloptr, version 1.2.1, License: LGPL-3

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