11 citations · 21 across the 19 of their papers we have counts for
20 papers
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…
Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem
Zhongzhu Chen, Marcia Fampa, Jon Lee
The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experime…
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…
Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem
Zhongzhu Chen, Marcia Fampa, Jon Lee
The best techniques for the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a variety of concave continu…
D-optimal Data Fusion: Exact and Approximation Algorithms
Yongchun Li, Marcia Fampa, Jon Lee +3
We study the D-optimal Data Fusion (DDF) problem, which aims to select new data points, given an existing Fisher information matrix, so as to maximize the logarithm of the determin…
On computing with some convex relaxations for the maximum-entropy sampling problem
Zhongzhu Chen, Marcia Fampa, Jon Lee
Based on a factorization of an input covariance matrix, we define a mild generalization of an upper bound of Nikolov (2015) and Li and Xie (2020) for the NP-Hard constrained maximu…
Tridiagonal Maximum-Entropy Sampling and Tridiagonal Masks
Hessa Al-Thani, Jon Lee
The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)determinant principal submatrix, of a given order, from an input covariance matrix . We give an efficie…
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…
1-norm minimization and minimum-rank structured sparsity for symmetric and ah-symmetric generalized inverses: rank one and two
Luze Xu, Marcia Fampa, Jon Lee
Generalized inverses are important in statistics and other areas of applied matrix algebra. A \emph{generalized inverse} of a real matrix is a matrix that satisfies the Moo…
An R Package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data
Hessa Al-Thani, Jon Lee
We present an open-source R package (MESgenCov v 0.1.0) for temporally fitting multivariate precipitation chemistry data and extracting a covariance matrix for use in the MESP (max…
Mixing convex-optimization bounds for maximum-entropy sampling
Zhongzhu Chen, Marcia Fampa, Amélie Lambert +1
The maximum-entropy sampling problem is a fundamental and challenging combinatorial-optimization problem, with application in spatial statistics. It asks to find a maximum-determin…
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…
Approximate 1-norm minimization and minimum-rank structured sparsity for various generalized inverses via local search
Luze Xu, Marcia Fampa, Jon Lee +1
Fundamental in matrix algebra and its applications, a \emph{generalized inverse} of a real matrix is a matrix that satisfies the Moore-Penrose (M-P) property . If $H…
On Sparse Reflexive Generalized Inverses
Marcia Fampa, Jon Lee
We study sparse generalized inverses of a rank- real matrix . We give a construction for reflexive generalized inverses having at most nonzeros. For and for $…
Efficient treatment of bilinear forms in global optimization
Marcia Fampa, Jon Lee
We efficiently treat bilinear forms in the context of global optimization, by applying McCormick convexification and by extending an approach of Saxena, Bonami and Lee for symmetri…
Sparse pseudoinverses via LP and SDP relaxations of Moore-Penrose
Victor K. Fuentes, Marcia Fampa, Jon Lee
Pseudoinverses are ubiquitous tools for handling over- and under-determined systems of equations. For computational efficiency, sparse pseudoinverses are desirable. Recently, spars…