dobj.dxt.tomogravity: Analytic gradient of objective function of Zhang et al. 2003

In awblocker/networkTomography: Tools for Network Tomography

Analytic gradient of objective function of Zhang et al. 2003

Description

Requires bounded optimization to maintain positive OD flows, and only those flows that are not deterministically zero should be included in the estimation.

Usage

dobj.dxt.tomogravity(xt, yt, A, srcDstInd, lambda)

Arguments

xt	length-k numeric vector of point-to-point flows
yt	length-m numeric vector of observed aggregate flows
A	m x k routing matrix, yt = A xt
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.

Value

numeric vector of length k containing gradient of objective function with respect to xt

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