Mathematical Non-Linear Programming.
rsolnpNLP(start, objective,
lower=0, upper=1, linCons, funCons, control=list())
solnpNLP(start, objective,
par.lower=NULL, par.upper=NULL,
eqA=NULL, eqA.bound=NULL,
ineqA=NULL, ineqA.lower=NULL, ineqA.upper=NULL,
eqFun=list(), eqFun.bound=NULL,
ineqFun=list(), ineqFun.lower=NULL, ineqFun.upper=NULL,
control=list())
solnpNLPControl(
rho=1, outer.iter=400, inner.iter=800, delta=1e-07, tol=1e-08, trace=0)
rnlminb2NLP(start, objective,
lower=0, upper=1, linCons, funCons, control=list())
nlminb2NLP(start, objective,
par.lower=NULL, par.upper=NULL,
eqA=NULL, eqA.bound=NULL,
ineqA=NULL, ineqA.lower=NULL, ineqA.upper=NULL,
eqFun=list(), eqFun.bound=NULL,
ineqFun=list(), ineqFun.lower=NULL, ineqFun.upper=NULL,
control=list())
nlminb2NLPControl(
eval.max=500, iter.max=400, trace=0, abs.tol=1e-20, rel.tol=1e-10,
x.tol=1.5e-08, step.min=2.2e-14, scale=1, R=1, beta.tol=1e-20)
rnlminb2
ramplNLP(start, objective,
lower=0, upper=1, amplCons, control=list(), ...)
amplNLP()
amplNLPControl(
solver="minos", project="ampl", trace=FALSE)
a list of class solver with the following named ebtries:
opt,
solution,
objective,
status,
message,
solver,
version.
a numeric vector, the start values.
a function object, the function to be optimized.
lower and upper bounds.
list of linear constraints: mat, lower, upper.
list of function constraints.
AMPL constraints.
control list.
optional arguments to be passed.
...
...
...
...
...
...
...
...
...
1
400
800
1.0e-7
1.0e-8
500
400
0
1e-20
1e-10
1.5e-08
2.2e-14
1
1
1e-20
solver name
project name
Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.