6 papers
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…
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…
On the relationship between MESP and 0/1 D-Opt and their upper bounds
Gabriel Ponte, Marcia Fampa, Jon Lee
We establish strong connections between two fundamental nonlinear 0/1 optimization problems coming from the area of experimental design, namely maximum-entropy sampling and 0/1 D-o…
Good and Fast Row-Sparse ah-Symmetric Reflexive Generalized Inverses
Gabriel Ponte, Marcia Fampa, Jon Lee +1
We present several algorithms aimed at constructing sparse and structured sparse (row-sparse) generalized inverses, with application to the efficient computation of least-squares s…
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…