Control variables for Rdonlp2

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Description

Collection of Control Variables

Usage

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donlp2Control( 
    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, 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 = TRUE, 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 tau0 diminishes the efficiency of DONLP2, while a large tau0 may degarde the reliability of the code.

tau

gives a weight between descent for 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 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 (nreset.multiplier times 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.