1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
|
\name{nlminb2}
\alias{nlminb2}
\title{Constrained nonlinear minimization}
\description{
Solve constrained nonlinear minimization
problem with nonlinear constraints using
a penalty and barrier approach.
}
\usage{
nlminb2(start, objective, eqFun = NULL, leqFun = NULL,
lower = -Inf, upper = Inf, gradient = NULL, hessian = NULL,
control = list(), env = .GlobalEnv)
}
\arguments{
\item{start}{
a numeric vector, initial values for the parameters to be
optimized.}
\item{objective}{
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 \code{start}. Further arguments (fixed during the course
of the optimization) to objective may be specified as well.
see \code{env}. }
\item{eqFun}{
a list of functions describing equal constraints.}
\item{leqFun}{
a list of functions describing less equal constraints.}
\item{lower, upper}{
two vectors of lower and upper bounds, replicated to be as long
as \code{start}. If unspecified, all parameters are assumed to
be unconstrained.}
\item{gradient}{
an optional function that takes the same arguments as \code{objective}
and evaluates the gradient of \code{objective} at its first argument.
Must return a vector as long as \code{start}.}
\item{hessian}{
an optional function that takes the same arguments as \code{objective}
and evaluates the hessian of \code{objective} at its first argument.
Must return a square matrix of order \code{length(start)}. Only the
lower triangle is used.}
\item{control}{
a list of control parameters. See below for details.}
\item{env}{
the environment in which objective, constraint, control
functions are evaluated.}
}
\value{
A list with following elements:
\item{par}{
a numeric vector, the best set of parameters found.}
\item{objective}{
a numeric value, the value of \code{objective} corresponding
to \code{par}.}
\item{convergence}{
an integer code, 0 indicates successful convergence.}
\item{message}{
a character string giving any additional information returned
by the optimizer, or NULL. For details, see PORT documentation.}
\item{iterations}{
am integer value, the number of iterations performed.}
\item{evaluations}{
an integer value, the number of objective function and gradient
function evaluations.}
}
\author{
For the R port of \code{nlminb} Douglas Bates and Deepayan Sarkar,
for the R/Rmetrics port of \code{nlminb2} Diethelm Wuertz,
for the PORT library netlib.bell-labs.com.
}
\references{
Paul A. Jensen & Jonathan F. Bard,
Operations Research Models and Methods, 2001
Appendix A, Algorithms for Constrained Optimization,
\url{https://www.me.utexas.edu/~jensen/ORMM/supplements/index.html}.
PORT Library,
\url{https://netlib.org/port/}.
}
\keyword{optimize}
|
