27 citations · 48 across the 29 of their papers we have counts for
34 papers
Mixed-Integer Second-Order Cone Formulations for the Euclidean Bottleneck Steiner Tree Problem in
Gabriel P. Braun, Marcia Fampa, Nelson Maculan +1
Given a set of terminals in and an integer , the Euclidean bottleneck Steiner tree problem (EBSTP) asks for a tree spanning the terminals with at most Ste…
On the 100% Rule of Linear Programming
Marcia Fampa, Jon Lee
The so-called ``100% rule'' is a somewhat classical and yet somehow obscure rule that gives some local sensitivity-analysis information based on the very limited local sensitivity-…
Faster dynamic programming for tridiagonal maximum-entropy sampling
Marcia Fampa, Jon Lee
The maximum-entropy sampling problem (MESP) seeks, for an order- covariance matrix , a principal submatrix of order with maximum log-determinant. Mostly for convenience,…
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…
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…
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…
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…
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…
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…
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…