11 papers
On computing sparse universal solvers for key problems in statistics
Ananias Sousa Machado, Marcia Fampa, Jon Lee
We give sparsity results and present algorithms for calculating minimum (vector) 1-norm universal solvers connected to least-squares problems. In particular, besides universal leas…
ADMM for 0/1 D-optimality and Maximum-Entropy Sampling Relaxations
Gabriel Ponte, Marcia Fampa, Jon Lee +1
The 0/1 D-optimality problem and the Maximum-Entropy Sampling problem are two well-known NP-hard discrete maximization problems in experimental design. Algorithms for exact optimiz…
The hyper-scaled NLP bound for maximum-entropy remote sampling
Gabriel Ponte, Marcia Fampa, Jon Lee
The maximum-entropy remote sampling problem (MERSP) is to select a subset of random variables from a set of random variables, so as to maximize the information concerning a…
Sparse symmetric generalized inverses for sparse symmetric matrices
Ananias Machado, Marcia Fampa, Jon Lee
Generalized inverses play a fundamental role in numerical linear algebra, particularly when matrices are rectangular, singular, or rank deficient. Even when the input matrix is spa…
Extended-variable relaxations for the constrained generalized maximum-entropy sampling problem
Gabriel Ponte, Kurt Anstreicher, Marcia Fampa +1
The constrained generalized maximum-entropy sampling problem (CGMESP) is to select an order-s principal submatrix from an order-n covariance matrix, subject to some linear side con…
Convex relaxation for the generalized maximum-entropy sampling problem
Gabriel Ponte, Marcia Fampa, Jon Lee
The generalized maximum-entropy sampling problem (GMESP) is to select an order- principal submatrix from an order- covariance matrix, to maximize the product of its great…