| constrOptim {stats} | R Documentation |
Minimise a function subject to linear inequality constraints using an adaptive barrier algorithm.
constrOptim(theta, f, grad, ui, ci, mu = 1e-04, control = list(),
method = if(is.null(grad)) "Nelder-Mead" else "BFGS",
outer.iterations = 100, outer.eps = 1e-05, ...)
theta |
Starting value: must be in the feasible region. |
f |
Function to minimise. |
grad |
Gradient of f. |
ui |
Constraints (see below). |
ci |
Constraints (see below). |
mu |
(Small) tuning parameter. |
control |
Passed to optim. |
method |
Passed to optim. |
outer.iterations |
Iterations of the barrier algorithm. |
outer.eps |
Criterion for relative convergence of the barrier algorithm. |
... |
Other arguments passed to optim |
The feasible region is defined by ui %*% theta - ci >= 0. The
starting value must be in the interior of the feasible region, but the
minimum may be on the boundary.
A logarithmic barrier is added to enforce the constraints and then
optim is called. The barrier function is chosen so that
the objective function should decrease at each outer iteration. Minima
in the interior of the feasible region are typically found quite
quickly, but a substantial number of outer iterations may be needed
for a minimum on the boundary.
The tuning parameter mu multiplies the barrier term. Its precise
value is often relatively unimportant. As mu increases the
augmented objective function becomes closer to the original objective
function but also less smooth near the boundary of the feasible
region.
Any optim method that permits infinite values for the objective
function may be used (currently all but "L-BFGS-B"). The gradient
function must be supplied except with method="Nelder-Mead".
As with optim, the default is to minimise and maximisation can
be performed by setting control$fnscale to a negative value.
As for optim, but with two extra components:
barrier.value giving the value of the barrier function at the
optimum and outer.iterations gives the
number of outer iterations (calls to optim)
K. Lange Numerical Analysis for Statisticians. Springer 2001, p185ff
optim, especially method="L-BGFS-B" which
does box-constrained optimisation.
## from optim
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
grr <- function(x) { ## Gradient of 'fr'
x1 <- x[1]
x2 <- x[2]
c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
200 * (x2 - x1 * x1))
}
optim(c(-1.2,1), fr, grr)
#Box-constraint, optimum on the boundary
constrOptim(c(-1.2,0.9), fr, grr, ui=rbind(c(-1,0),c(0,-1)), ci=c(-1,-1))
# x<=0.9, y-x>0.1
constrOptim(c(.5,0), fr, grr, ui=rbind(c(-1,0),c(1,-1)), ci=c(-0.9,0.1))
## Solves linear and quadratic programming problems
## but needs a feasible starting value
#
# from example(solve.QP) in 'quadprog'
# no derivative
fQP <- function(b) {-sum(c(0,5,0)*b)+0.5*sum(b*b)}
Amat <- matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3)
bvec <- c(-8,2,0)
constrOptim(c(2,-1,-1), fQP, NULL, ui=t(Amat),ci=bvec)
# derivative
gQP <- function(b) {-c(0,5,0)+b}
constrOptim(c(2,-1,-1), fQP, gQP, ui=t(Amat), ci=bvec)
## Now with maximisation instead of minimisation
hQP <- function(b) {sum(c(0,5,0)*b)-0.5*sum(b*b)}
constrOptim(c(2,-1,-1), hQP, NULL, ui=t(Amat), ci=bvec,
control=list(fnscale=-1))