9 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…
The dual-path fixing strategy and its application to the set-covering problem
Paulo Michel F. Yamagishi, Marcia Fampa, Jon Lee
We introduce the dual-path fixing strategy to exploit dual algorithms for solving relaxations of mixed-integer nonlinear-optimization problems. Such dual algorithms are naturally a…