550,952 research outputs found
Tensor decomposition and homotopy continuation
A computationally challenging classical elimination theory problem is to
compute polynomials which vanish on the set of tensors of a given rank. By
moving away from computing polynomials via elimination theory to computing
pseudowitness sets via numerical elimination theory, we develop computational
methods for computing ranks and border ranks of tensors along with
decompositions. More generally, we present our approach using joins of any
collection of irreducible and nondegenerate projective varieties
defined over . After computing
ranks over , we also explore computing real ranks. Various examples
are included to demonstrate this numerical algebraic geometric approach.Comment: We have added two examples: A Coppersmith-Winograd tensor, Matrix
multiplication with zeros. (26 pages, 1 figure
On Computing the Elimination Ideal Using Resultants with Applications to Gr\"obner Bases
Resultants and Gr\"obner bases are crucial tools in studying polynomial
elimination theory. We investigate relations between the variety of the
resultant of two polynomials and the variety of the ideal they generate. Then
we focus on the bivariate case, in which the elimination ideal is principal. We
study - by means of elementary tools - the difference between the multiplicity
of the factors of the generator of the elimination ideal and the multiplicity
of the factors of the resultant.Comment: 7 page
Quantifier Elimination over Finite Fields Using Gr\"obner Bases
We give an algebraic quantifier elimination algorithm for the first-order
theory over any given finite field using Gr\"obner basis methods. The algorithm
relies on the strong Nullstellensatz and properties of elimination ideals over
finite fields. We analyze the theoretical complexity of the algorithm and show
its application in the formal analysis of a biological controller model.Comment: A shorter version is to appear in International Conference on
Algebraic Informatics 201
Feature Selection via Coalitional Game Theory
We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets
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