Solve constrained nonlinear minimization problem with nonlinear constraints using a penalty and barrier approach.
nlminb2(start, objective, eqFun = NULL, leqFun = NULL,
lower = -Inf, upper = Inf, gradient = NULL, hessian = NULL,
control = list(), env = .GlobalEnv)
A list with following elements:
a numeric vector, the best set of parameters found.
a numeric value, the value of objective corresponding
to par.
an integer code, 0 indicates successful convergence.
a character string giving any additional information returned by the optimizer, or NULL. For details, see PORT documentation.
am integer value, the number of iterations performed.
an integer value, the number of objective function and gradient function evaluations.
a numeric vector, initial values for the parameters to be optimized.
function to be minimized. Must return a scalar value (possibly
NA/Inf). The first argument to objective is the vector of
parameters to be optimized, whose initial values are supplied
through start. Further arguments (fixed during the course
of the optimization) to objective may be specified as well.
see env.
a list of functions describing equal constraints.
a list of functions describing less equal constraints.
two vectors of lower and upper bounds, replicated to be as long
as start. If unspecified, all parameters are assumed to
be unconstrained.
an optional function that takes the same arguments as objective
and evaluates the gradient of objective at its first argument.
Must return a vector as long as start.
an optional function that takes the same arguments as objective
and evaluates the hessian of objective at its first argument.
Must return a square matrix of order length(start). Only the
lower triangle is used.
a list of control parameters. See below for details.
the environment in which objective, constraint, control functions are evaluated.
For the R port of nlminb Douglas Bates and Deepayan Sarkar,
for the R/Rmetrics port of nlminb2 Diethelm Wuertz,
for the PORT library netlib.bell-labs.com.
Paul A. Jensen & Jonathan F. Bard, Operations Research Models and Methods, 2001 Appendix A, Algorithms for Constrained Optimization, https://www.me.utexas.edu/~jensen/ORMM/supplements/index.html.
PORT Library, https://netlib.org/port/.