搜索结果: 1-7 共查到“统计核算理论 optimization.”相关记录7条 . 查询时间(0.135 秒)
Optimization with First-Order Surrogate Functions
Optimization First-Order Surrogate Functions
2013/6/14
In this paper, we study optimization methods consisting of iteratively minimizing surrogates of an objective function. By proposing several algorithmic variants and simple convergence analyses, we mak...
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A General Iterative Shrinkage Thresholding Algorithm Non-convex Regularized Optimization Problems
2013/5/2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Optimization viewpoint Kalman smoothing applications robust sparse estimation
2013/4/28
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate ...
Matrix completion via max-norm constrained optimization
Compressed sensing low-rank matrix matrix completion max-norm con-strained minimization optimal rate of convergence sparsity
2013/4/28
This paper studies matrix completion under a general sampling model using the max-norm as a convex relaxation for the rank of the matrix. The optimal rate of convergence is established for the Frobeni...
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
data fusion matrix factorizations tensor factorizations CANDECOMP PARAFAC missing data
2011/6/21
Joint analysis of data from multiple sources has the potential
to improve our understanding of the underlying structures
in complex data sets. For instance, in restaurant recommendation
systems, re...
Generalized Boosting Algorithms for Convex Optimization
Generalized Boosting Algorithms Convex Optimization
2011/6/21
Boosting is a popular way to derive power-
ful learners from simpler hypothesis classes.
Following previous work (Mason et al., 1999;
Friedman, 2000) on general boosting frame-
works, we analyze g...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns ...