ADMM_EN_SMW | R Documentation |

Applies Alternating Direction Method of Multipliers to the l1-regularized quadratic program

`f(\mathbf{x}) + g(\mathbf{x}) = \frac{1}{2}\mathbf{x}^TA\mathbf{x} - d^T\mathbf{x} + \lambda |\mathbf{x}|_1`

```
ADMM_EN_SMW(Ainv, V, R, d, x0, lam, mu, maxits, tol, quiet, selector)
```

`Ainv` |
Diagonal of |

`V` |
Matrix from SMW formula. |

`R` |
Upper triangular matrix in Cholesky decomposition of |

`d` |
nx1 dimensional column vector. |

`lam` |
Regularization parameter for l1 penalty, must be greater than zero. |

`mu` |
Augmented Lagrangian penalty parameter, must be greater than zero. |

`maxits` |
Number of iterations to run |

`tol` |
Vector of stopping tolerances, first value is absolute, second is relative tolerance. |

`quiet` |
Logical controlling display of intermediate statistics. |

`selector` |
Vector to choose which parameters in the discriminant vector will be used to calculate the regularization terms. The size of the vector must be *p* the number of predictors. The default value is a vector of all ones. This is currently only used for ordinal classification. |

This function is used by other functions and should only be called explicitly for debugging purposes.

`ADMM_EN_SMW`

returns an object of `class`

"`ADMM_EN_SMW`

" including a list
with the following named components

`call`

The matched call.

`x`

Found solution.

`y`

Dual solution.

`z`

Slack variables.

`k`

Number of iterations used.

Used by: `SDAD`

and the `SDADcv`

cross-validation version.

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