donlp2Control {fPortfolio}R Documentation

Control variables for Rdonlp2

Description

Collection of Control Variables

Usage

rdonlp2Control(iterma = 4000, nstep = 20,fnscale = 1, report = FALSE, 
    rep.freq = 1, tau0 = 1.0, tau = 0.1, del0 = 1.0, epsx = 1e-5,
    delmin = 0.1*del0, epsdif = 1e-8, nreset.multiplier = 1,
    difftype = 3,epsfcn = 1e-16,taubnd = 1.0, hessian = FALSE,
    te0 = TRUE, te1 = FALSE, te2 = FALSE, te3 = FALSE, silent = FALSE, 
    intakt = TRUE)

Arguments

iterma maximum number of iterations.
nstep maximum number of tries in the backtracking.
fnscale set -1 to maximize the object function.
report If TRUE, a list object which contains detailed information will be passed to control.fun() in donlp2.
rep.freq the frequency of report(positive integer). the report will be passed to control.fun every rep.freq iterations.
tau0 the positive amount how much any constaint other than abound can be violated. A small texttt{tau0} diminishes the efficiency of DONLP2, while a large texttt{tau0} may degarde the reliability of the code.
tau gives a weight between descent for texttt{fn} and infeasibility and is also used as a safety parameter for chosing the penalty weigths. It can be chosen larger zero at will, but useful values are between 0.1 and 1. The smaller tau, the more may texttt{fn} be scaled down. Tau is also used as an additive increase for the penalty-weights. Therefore it should not be chosen too large, since that degrades the performance.
del0 The positive amount by which constraints are considered binding. If too small, the indentification of correct sets of binding constraints may be delayed. If too large, DONLP2 will escape to the full regularlized SQP method(quite costly). Good values are in [0.01, 1.0].
epsx successful temination is indicated if the Kuhn-Tucker criteria are satisfied within the value.
delmin constraints are considered as sufficiently satisfied if absolute values of their violation are less than the value.
epsdif relative precision in the gradients.
nreset.multiplier maximum number of steps (texttt{nreset.multiplier} times texttt{n}) until a ``restart'' of the accumulated quasi-newton-update is tried. Value should be integer between 1 and 4.
difftype 1,2,3. numerical differentiation algorithm. The algorithm with difftype=2 is nearly identical to one used in optim(). See PDF manual attached in this package.
epsfcn relative precision of the function evaluation routine.
taubnd The positive amount by which bounds may be violated if numerical differention is used.
hessian if TRUE, numeric Hessian matrix is calculated by numerical differentiation algorithm specified in difftype.
intakt if TRUE, informations about current iteration step in optimization and final results are output to R console. The amount of information depends on te0, te1, te2, te3.
te0 if TRUE one-line-output for every step is printed.
te1 if TRUE post-mortem-dump of accumlated information is printed.
te2 if TRUE, more detailed information is ``pretty-printed''.
te3 if TRUE, the gradients and approximated Newton-Raphson updates(in upper triangler matrix) are printed.
silent If TRUE, donlp2() runs quietly, i.e., intakt=FALSE, .pro/.mes files are not created, and te0,te1,te2,te3 are omitted.

See Also

rdonlp2


[Package fPortfolio version 260.72 Index]