Linearly Constrained Optimization
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, ..., hessian = FALSE)
- numeric (vector) starting value (of length $p$): must be in the feasible region.
- function to minimise (see below).
- gradient of
functionas well), or
- constraint matrix ($k x p$), see below.
- constraint vector of length $k$ (see below).
- (Small) tuning parameter.
- control, method, hessian
- passed to
- iterations of the barrier algorithm.
- non-negative number; the relative convergence tolerance of the barrier algorithm.
- Other named arguments to be passed to
grad: needs to be passed through
optimso should not match its argument names.
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
optim method that permits infinite values for the
objective function may be used (currently all but "L-BFGS-B").
The objective function
f takes as first argument the vector
of parameters over which minimisation is to take place. It should
return a scalar result. Optional arguments
... will be
optim and then (if not used by
f. As with
optim, the default is to minimise, but
maximisation can be performed by setting
control$fnscale to a
The gradient function
grad must be supplied except with
method = "Nelder-Mead". It should take arguments matching
f and return a vector containing the gradient.
K. Lange Numerical Analysis for Statisticians. Springer 2001, p185ff
method = "L-BFGS-B" which
does box-constrained optimisation.