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Improved Mixing and Pressure Loss Formulations for Gas Network Optimization
Geonhee Kim, Christopher Lourenco, Daphne Skipper +1
Non-convex, nonlinear gas network optimization models are used to determine the feasibility of flows on existing networks given constraints on network flows, gas mixing, and pressu…
On the column number and forbidden submatrices for -modular matrices
Joseph Paat, Ingo Stallknecht, Zach Walsh +1
An integer matrix is -modular if the determinant of each submatrix of has absolute value at mo…
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…
Gaining or Losing Perspective for Piecewise-Linear Under-Estimators of Convex Univariate Functions
Jon Lee, Daphne Skipper, Emily Speakman +1
We study MINLO (mixed-integer nonlinear optimization) formulations of the disjunction , where is a binary indicator of (),…
Improving proximity bounds using sparsity
Jon Lee, Joseph Paat, Ingo Stallknecht +1
We refer to the distance between optimal solutions of integer programs and their linear relaxations as proximity. In 2018, Eisenbrand and Weismantel proved that proximity is indepe…
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…