An Augmented Lagrangian Approach for Sparse Principal Component Analysis (2009)
Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from...
Smooth Optimization Approach for Sparse Covariance Selection (2009)
In this paper we first study a smooth optimization approach for solving a class of nonsmooth strictly concave maximization problems whose objective functions admit smooth convex minimization...
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection (2009)
In this paper, we consider estimating sparse inverse covariance of a Gaussian graphical model whose conditional independence is assumed to be partially known. Similarly as in [5], we formulate it as...
Lu, Zhaosong, Monteiro, Renato D. C., Yuan, Ming
In this paper, we study convex optimization methods for computing the trace norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection...
The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a...
The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a...
The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a...
The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a...
Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006.
Optimal Solutions for the Closest-String Problem via Integer Programming (2004)
Claudio N. Meneses, Zhaosong Lu, Panos M. Pardalos
this paper we study the closest-string problem (CSP), which can be defined as follows: given a finite set of strings, each string with length m, find a center string t of length m minimizing d, such...
Optimal solutions for the closest-string problem via integer programming (2004)
Cláudio N. Meneses, Zhaosong Lu, Panos M. Pardalos
informs ® doi 10.1287/ijoc.1040.0090 © 2004 INFORMS In this paper we study the closest-string problem (CSP), which can be defined as follows: Given a finite set � = �s1�s2�����sn...
Global optimization by using interval analysis method / (2000)
Thesis (M.A.)--University of Alabama, 2000.
Includes bibliographical references (leaves 103-118).
Dimension reduction and coefficient estimation in multivariate linear regression
Ming Yuan, Ali Ekici, Zhaosong Lu, Renato Monteiro
We introduce a general formulation for dimension reduction and coefficient estimation in the multivariate linear model. We argue that many of the existing methods that are commonly used in practice...