dantzig.generator

Generates sparse linear regression model for testing <code>dantzig</code> function

Provides a method of recovering the precision matrix efficiently
and solving for the dantzig selector by applying the parametric
simplex method.  The computation is based on a linear optimization
solver. It also contains a generic LP solver and a parameterized LP
solver using parametric simplex method.

ORPHANED

fastclime

A Fast Solver for Parameterized LP Problems, Constrained L1
Minimization Approach to Sparse Precision Matrix Estimation and
Dantzig Selector

dantzig.generator function

<dl> <dt>n</dt>
<dd>The number of observations (sample size). The default value is <code>50</code>.</dd> <dt>d</dt>
<dd>The number of variables (dimension). The default value is <code>100</code>.</dd> <dt>sparsity</dt>
<dd><code>d</code> is either the number of nonzero entries out of <code>d</code> or the proportion of nonzero entries in <code>BETA</code></dd> <dt>sigma0</dt>
<dd><code>sigma0</code> is the standard deviation of the noise vector</dd></dl>

Arguments

Haotian Pang, Han Liu, Robert Vanderbei and Di Qi 
Maintainer: Di Qi &lt;dqi@princeton.edu&gt;

Author

Dantzig data generator — dantzig.generator

<dl>

 <dt>n</dt>
<dd>The number of observations (sample size). The default value is <code>50</code>.</dd>

 <dt>d</dt>
<dd>The number of variables (dimension). The default value is <code>100</code>.</dd>

 <dt>sparsity</dt>
<dd><code>d</code> is either the number of nonzero entries out of <code>d</code> or the proportion of nonzero entries in <code>BETA</code></dd>

 <dt>sigma0</dt>
<dd><code>sigma0</code> is the standard deviation of the noise vector</dd>

</dl>

Haotian Pang, Han Liu, Robert Vanderbei and Di Qi 
Maintainer: Di Qi &lt;dqi@princeton.edu&gt;

dantzig.generator: Dantzig data generator

Description

Usage

Value

Arguments

Author

Details

See Also

Examples