Positive-Definite Sparse Precision Matrix Estimation  

作　　者：Lin Xia Xudong Huang Guanpeng Wang Tao Wu 

机构地区：[1]School of Mathematics and Computer Science, Anhui Normal University, Wuhu, China [2]School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China

出　　处：《Advances in Pure Mathematics》2017年第1期21-30,共10页理论数学进展（英文）

摘　　要：The positive-definiteness and sparsity are the most important property of high-dimensional precision matrices. To better achieve those property, this paper uses a sparse lasso penalized D-trace loss under the positive-definiteness constraint to estimate high-dimensional precision matrices. This paper derives an efficient accelerated gradient method to solve the challenging optimization problem and establish its converges rate as . The numerical simulations illustrated our method have competitive advantage than other methods.The positive-definiteness and sparsity are the most important property of high-dimensional precision matrices. To better achieve those property, this paper uses a sparse lasso penalized D-trace loss under the positive-definiteness constraint to estimate high-dimensional precision matrices. This paper derives an efficient accelerated gradient method to solve the challenging optimization problem and establish its converges rate as . The numerical simulations illustrated our method have competitive advantage than other methods.

关 键 词：Positive-Definiteness SPARSITY D-Trace Loss ACCELERATED Gradient Method 

分 类 号：O1[理学—数学]