Solve Ax=b by preconditioned conjugate gradients

internal

Fit multi-level models with possibly correlated
random effects using Markov Chain Monte Carlo simulation.
Such models allow smoothing over space and time and are useful in,
for example, small area estimation.

Harm Jan Boonstra

mcmcsae

Markov Chain Monte Carlo Small Area Estimation

Grzegorz Baltissen

CG function

<dl><dt>b</dt>
<dd>right hand side vector.</dd>
<dt>env</dt>
<dd>environment containing at least a function <code>A_times</code> that computes
the matrix-vector product Ax for some input vector x, and a function <code>M_solve</code>
that computes M^-1 x for some preconditioner matrix M.</dd>
<dt>x</dt>
<dd>start value for the conjugate gradient algorithm.</dd>
<dt>max.it</dt>
<dd>maximum number of iterations.</dd>
<dt>e</dt>
<dd>total squared error stop criterion.</dd>
<dt>verbose</dt>
<dd>whether progress information is shown.</dd>
<dt>...</dt>
<dd>any parameters passed to <code>A_times</code> and <code>M_solve</code>.</dd></dl>

Arguments

Solve Ax=b by preconditioned conjugate gradients — CG

<dl>

<dt>b</dt>
<dd>right hand side vector.</dd>


<dt>env</dt>
<dd>environment containing at least a function <code>A_times</code> that computes
the matrix-vector product Ax for some input vector x, and a function <code>M_solve</code>
that computes M^-1 x for some preconditioner matrix M.</dd>


<dt>x</dt>
<dd>start value for the conjugate gradient algorithm.</dd>


<dt>max.it</dt>
<dd>maximum number of iterations.</dd>


<dt>e</dt>
<dd>total squared error stop criterion.</dd>


<dt>verbose</dt>
<dd>whether progress information is shown.</dd>


<dt>...</dt>
<dd>any parameters passed to <code>A_times</code> and <code>M_solve</code>.</dd>

</dl>

CG: Solve Ax=b by preconditioned conjugate gradients

Description

Usage

Value

Arguments

References