bobyqa
An R interface to the bobyqa implementation of Powell
The purpose of bobyqa is to minimize a function of many variables
by a trust region method that forms quadratic models by interpolation.
Box constraints (bounds) on the parameters are permitted.
Usage
bobyqa(par, fn, lower = -Inf, upper = Inf, control = list(), ...)Arguments
- par
- A numeric vector of starting estimates of the parameters of the objective function.
- fn
- A function that returns the value of the objective at the
supplied set of parameters
parusing auxiliary data in .... The first argument offnmust bepar. - lower
- A numeric vector of lower bounds on the parameters. If the length is 1 the single lower bound is applied to all parameters.
- upper
- A numeric vector of upper bounds on the parameters. If the length is 1 the single upper bound is applied to all parameters.
- control
- An optional list of control settings. See the details section for the names of the settable control values and their effect.
- ...
- Further arguments to be passed to
fn.
Details
The function fn must return a scalar numeric value.
The control argument is a list. Possible named values in the
list and their defaults are:
[object Object],[object Object],[object Object],[object Object],[object Object]
Value
- A list with components:
par The best set of parameters found. fval The value of the objective at the best set of parameters found. feval The number of function evaluations used. ierr An integer error code. A value of zero indicates success. Other values are [object Object],[object Object],[object Object],[object Object],[object Object]
encoding
UTF-8
References
M. J. D. Powell (2007)
"Developments of NEWUOA for unconstrained minimization without
derivatives",
Cambridge University, Department of Applied Mathematics and
Theoretical Physics,
Numerical Analysis Group, Report NA2007/05,
M. J. D. Powell (2009), "The BOBYQA algorithm for bound constrained
optimization without derivatives", Report No. DAMTP 2009/NA06,
Centre for Mathematical Sciences, University of Cambridge, UK.
See Also
Examples
fr <- function(x) { ## Rosenbrock Banana function
100 * (x[2] - x[1]^2)^2 + (1 - x[1])^2
}
(x1 <- bobyqa(c(1, 2), fr, lower = c(0, 0), upper = c(4, 4)))
## => optimum at c(1, 1) with fval = 0
str(x1) # see that the error code and msg are returned
# check the error exits
# too many iterations
x1e<-bobyqa(c(1, 2), fr, lower = c(0, 0), upper = c(4, 4), control = list(maxfun=50))
str(x1e)
# Throw an error because bounds too tight
x1b<-bobyqa(c(4,4), fr, lower = c(0, 3.9999999), upper = c(4, 4))
str(x1b)
# Throw an error because npt is too small -- does NOT work as of 2010-8-10 as
# minqa.R seems to force a reset.
x1n<-bobyqa(c(2,2), fr, lower = c(0, 0), upper = c(4, 4), control=list(npt=1))
str(x1n)
# To add if we can find them -- examples of ierr = 3 and ierr = 5.