DESP_PEN_grad: Steepest descent algorithm for penalized maximum likelihood...

Description Usage Arguments Value Author(s)

View source: R/DESP_PEN_grad.R

Description

This function implements the steepest descent algorithm with adaptative stepsize and scaled descent direction to solve the maximum likelihood optimization problem and get the diagonal of the precision matrix.

Usage

1
  DESP_PEN_grad(S, B, init, kappa, thresh, stepsize, tol)

Arguments

S

The sample covariance matrix.

B

The coefficient matrix.

init

The starting vector of the iteration.

kappa

The tunning paramater.

thresh

The threshold level.

stepsize

The initial step-size.

tol

The gradient magnitude tolerance.

Value

This function returns the diagonal of the precision matrix associated with the sample covariance matrix S as a vector.

Author(s)

Arnak Dalalyan and Samuel Balmand.


DESP documentation built on May 29, 2017, 9:27 p.m.

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