mathprog-NLP | R Documentation |
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)
start |
a numeric vector, the start values. |
objective |
a function object, the function to be optimized. |
lower, upper |
lower and upper bounds. |
linCons |
list of linear constraints: mat, lower, upper. |
funCons |
list of function constraints. |
amplCons |
AMPL constraints. |
control |
control list. |
... |
optional arguments to be passed. |
par.lower, par.upper |
... |
eqA |
... |
eqA.bound |
... |
ineqA |
... |
ineqA.lower,ineqA.upper |
... |
eqFun |
... |
eqFun.bound |
... |
ineqFun |
... |
ineqFun.lower,ineqFun.upper |
... |
rho |
1 |
outer.iter |
400 |
inner.iter |
800 |
delta |
1.0e-7 |
tol |
1.0e-8 |
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 |
solver |
solver name |
project |
project name |
a list of class solver
with the following named ebtries:
opt
,
solution
,
objective
,
status
,
message
,
solver
,
version
.
Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.
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