constrOptim

0th

Percentile

Linearly Constrained Optimization

Minimise a function subject to linear inequality constraints using an adaptive barrier algorithm.

Keywords
optimize
Usage
constrOptim(theta, f, grad, ui, ci, mu = 1e-04, control = list(),
outer.iterations = 100, outer.eps = 1e-05, …,
hessian = FALSE)
Arguments
theta
numeric (vector) starting value (of length $p$): must be in the feasible region.
f
function to minimise (see below).
gradient of f (a function as well), or NULL (see below).
ui
constraint matrix ($k \times p$), see below.
ci
constraint vector of length $k$ (see below).
mu
(Small) tuning parameter.
control, method, hessian
passed to optim.
outer.iterations
iterations of the barrier algorithm.
outer.eps
non-negative number; the relative convergence tolerance of the barrier algorithm.
Other named arguments to be passed to f and grad: needs to be passed through optim so should not match its argument names.

References

K. Lange Numerical Analysis for Statisticians. Springer 2001, p185ff

optim, especially method = "L-BFGS-B" which does box-constrained optimisation.