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 …

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 …

Sparse principal component analysis (SPCA) is designed to enhance the interpretability of
traditional principal component analysis by optimally selecting a subset of features that …

We consider the variable selection problem for two-sample tests, aiming to select the most
informative variables to distinguish samples from two groups. To solve this problem, we …

Based on a factorization of an input covariance matrix, we define a mild generalization of an
upper bound of Nikolov and of Li and Xie for the NP-hard constrained maximum-entropy …

We apply the well-known outer-approximation algorithm (OA) of convex mixed-integer
nonlinear optimization to the maximum-entropy sampling problem (MESP), using convex …

The maximum-entropy sampling problem is a fundamental and challenging combinatorial-
optimization problem, with application in spatial statistics. It asks to find a maximum …

The best practical techniques for exact solution of instances of the constrained maximum-
entropy sampling problem, a discrete-optimization problem arising in the design of …

The truncated singular value decomposition (SVD), also known as the best low-rank matrix
approximation with minimum error measured by a unitarily invariant norm, has been applied …

The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)
determinant principal submatrix, of a given order, from an input covariance matrix C. We give …