2 citations · 3 across the 11 of their papers we have counts for
13 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…
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…
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…
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…
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…
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…
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…
On computing sparse generalized inverses
Gabriel Ponte, Marcia Fampa, Jon Lee +1
The well-known M-P (Moore-Penrose) pseudoinverse is used in several linear-algebra applications; for example, to compute least-squares solutions of inconsistent systems of linear e…
Computing D-Optimal solutions for huge-scale linear and quadratic response-surface models
Gabriel Ponte, Marcia Fampa, Jon Lee
We consider algorithmic approaches to the D-optimality problem for cases where the input design matrix is large and highly structured, in particular implicitly specified as a full…
Branch-and-bound for integer D-Optimality with fast local search and variable-bound tightening
Gabriel Ponte, Marcia Fampa, Jon Lee
We develop a branch-and-bound algorithm for the integer D-optimality problem, a central problem in statistical design theory, based on two convex relaxations, employing variable-bo…
Branch-and-bound for D-Optimality with fast local search and variable-bound tightening
Gabriel Ponte, Marcia Fampa, Jon Lee
We apply a branch-and-bound (B\&B) algorithm to the D-optimality problem based on a convex mixed-integer nonlinear formulation. We discuss possible methodologies to accelerate the…
Trading off 1-norm and sparsity against rank for linear models using mathematical optimization: 1-norm minimizing partially reflexive ah-symmetric generalized inverses
Marcia Fampa, Jon Lee, Gabriel Ponte
The M-P (Moore-Penrose) pseudoinverse has as a key application the computation of least-squares solutions of inconsistent systems of linear equations. Irrespective of whether a giv…
Experimental analysis of local searches for sparse reflexive generalized inverses
Marcia Fampa, Jon Lee, Gabriel Ponte +1
The well-known M-P (Moore-Penrose) pseudoinverse is used in several linear-algebra applications; for example, to compute least-squares solutions of inconsistent systems of linear e…