7 citations · 12 across the 8 of their papers we have counts for
8 papers
New insights into the NLP-Id bound for maximum-entropy sampling
Kurt Anstreicher, Marcia Fampa, Jon Lee +2
We establish new properties of the NLP-Id upper bound for the maxi\-mum-entropy sampling problem (MESP). In particular, we give a detailed look at the concavity of its objective fu…
On Sparse Canonical Correlation Analysis
Yongchun Li, Santanu S. Dey, Weijun Xie
The classical Canonical Correlation Analysis (CCA) identifies the correlations between two sets of multivariate variables based on their covariance, which has been widely applied i…
On the Partial Convexification for Low-Rank Spectral Optimization: Rank Bounds and Algorithms
Yongchun Li, Weijun Xie
A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective subject to multiple two-sided linear matrix inequalities intersected with a low-rank and spectral const…
On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problems
Yongchun Li, Weijun Xie
In the rank-constrained optimization problem (RCOP), it minimizes a linear objective function over a prespecified closed rank-constrained domain set and generic two-sided linea…
D-optimal Data Fusion: Exact and Approximation Algorithms
Yongchun Li, Marcia Fampa, Jon Lee +3
We study the D-optimal Data Fusion (DDF) problem, which aims to select new data points, given an existing Fisher information matrix, so as to maximize the logarithm of the determin…
Beyond Symmetry: Best Submatrix Selection for the Sparse Truncated SVD
Yongchun Li, Weijun Xie
Truncated singular value decomposition (SVD), also known as the best low-rank matrix approximation, has been successfully applied to many domains such as biology, healthcare, and o…
Exact and Approximation Algorithms for Sparse PCA
Yongchun Li, Weijun Xie
Sparse PCA (SPCA) is a fundamental model in machine learning and data analytics, which has witnessed a variety of application areas such as finance, manufacturing, biology, healthc…
Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees
Yongchun Li, Weijun Xie
This paper studies a classic maximum entropy sampling problem (MESP), which aims to select the most informative principal submatrix of a prespecified size from a covariance matrix.…