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有限记忆拟牛顿和Hessianfree牛顿非光滑优化方法Limited-memory quasi-Newton and Hessianfree Newton methods for non-smooth optimization |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_schmidt_lmq/ |
| 主讲教师: | Mark Schmidt |
| 开课单位: | 不列颠哥伦比亚大学 |
| 开课时间: | 2011-01-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Limited-memory quasi-Newton and Hessian-free Newton methods are two workhorses of unconstrained optimization of high-dimensional smooth objectives. However, in many cases we would like to optimize a high-dimensional unconstrained objective function that is non-smooth due to the presence of a ‘simple’ non-smooth regularization term. Motivated by problems arising in estimating sparse graphical models, in this talk we focus on strategies for extending limited-memory quasi- Newton and Hessian-free Newton methods for unconstrained optimization to this scenario. We first consider two-metric (sub-) gradient projection methods for problems where the regularizer is separable, and then consider proximal Newton-like methods for group-separable and non-separable regularizers. We will discuss several applications where sparsity-encouraging regularizers are used to estimate graphical model parameters and/or structure, including the estimation of sparse, blockwise-sparse, and structured-sparse models. |
| 关 键 词: | 高维平滑目标; 无约束优化; 稀疏图形模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 63 |