tomogravity.fit: Tomogravity estimation for a single time point using L-BFGS-B

In awblocker/networkTomography: Tools for Network Tomography

Tomogravity estimation for a single time point using L-BFGS-B

Description

Tomogravity estimation for a single time point using L-BFGS-B

Usage

tomogravity.fit(
  yt,
  A,
  srcDstInd,
  lambda,
  N = 1,
  normalize = FALSE,
  lower = 0,
  control = list()
)

Arguments

yt	length-m numeric vector of observed aggregate flows at time t
A	m x k routing matrix
srcDstInd	list of source and destination flow indices corresponding to each point-to-point flow, as produced by getSrcDstIndices
lambda	regularization parameter for mutual information prior. Note that this is scaled by the squared total traffic in the objective function before scaling the mutual information prior.
N	total traffic for normalization. Unused if normalized is FALSE.
normalize	If TRUE, xt and yt are scaled by N. Typically used in conjunction with calcN to normalize traffic to proportions, easing the tuning of lambda.
lower	Component-wise lower bound for xt in L-BFGS-B optimization.
control	List of control information for optim.

Value

A list as returned by optim, with element par containing the estimated point-to-point flows and element gr containing the analytic gradient evaluated at the estimate.

See Also

Other tomogravity: tomogravity()

Examples

data(cmu)
srcDstInd <- getSrcDstIndices(cmu$A.full)
estimate <- tomogravity.fit(yt=cmu$Y.full[1, ], A=cmu$A.full,
     srcDstInd=srcDstInd, lambda=0.01)

awblocker/networkTomography documentation built on May 14, 2022, 10:05 p.m.