Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools

ICML

2007

IEEE

2007

IEEE

The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be used for an L1-regularized loss due to its non-differentiability whenever some parameter is zero. Efficient algorithms have been proposed for this task, but they are impractical when the number of parameters is very large. We present an algorithm OrthantWise Limited-memory Quasi-Newton (owlqn), based on l-bfgs, that can efficiently optimize the L1-regularized log-likelihood of log-linear models with millions of parameters. In our experiments on a parse reranking task, our algorithm was several orders of magnitude faster than an alternative algorithm, and substantially faster than lbfgs on the analogous L2-regularized problem. We also present a proof that owl-qn is guaranteed to converge to a globally optimal parameter vector.

Added |
17 Nov 2009 |

Updated |
17 Nov 2009 |

Type |
Conference |

Year |
2007 |

Where |
ICML |

Authors |
Galen Andrew, Jianfeng Gao |

Comments (0)