APG_EN2 | R Documentation |

Applies accelerated proximal gradient algorithm 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`

```
APG_EN2(A, d, x0, lam, alpha, maxits, tol, selector = rep(1, dim(x0)[1]))
```

`A` |
p by p positive definite coefficient matrix
. |

`d` |
nx1 dimensional column vector. |

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

`alpha` |
Step length. |

`maxits` |
Number of iterations to run |

`tol` |
Stopping tolerance for proximal gradient algorithm. |

`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.

`APG_EN2`

returns an object of `class`

"`APG_EN2`

" including a list
with the following named components

`call`

The matched call.

`x`

Found solution.

`k`

Number of iterations used.

Used by: `SDAAP`

and the `SDAAPcv`

cross-validation version.

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