12 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…
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…
Recent Advances in Maximum-Entropy Sampling
Marcia Fampa, Jon Lee
In 2022, we published the book Maximum-Entropy Sampling: Algorithms and Application (Springer). Since then, there have been several notable advancements on this topic. In this manu…
On a geometric graph-covering problem related to optimal safety-landing-site location
Claudia D'Ambrosio, Marcia Fampa, Jon Lee +1
We propose integer-programming formulations for an optimal safety-landing site (SLS) location problem that arises in the design of urban air-transportation networks. We first devel…
Computing Experiment-Constrained D-Optimal Designs
Aditya Pillai, Gabriel Ponte, Marcia Fampa +3
In optimal experimental design, the objective is to select a limited set of experiments that maximizes information about unknown model parameters based on factor levels. This work…