2022
DOI: 10.1007/s11856-022-2364-9
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Lower bounds for contingency tables via Lorentzian polynomials

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Cited by 9 publications
(8 citation statements)
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“…By [21, Lemma 4.8], this is indeed a strengthening of the statement that () is denormalized Lorentzian. Even when D$\mathrm{D}$ has an enveloping matroid, we do not know if there is a denormalized Lorentzian evaluation of the multivariable U$U$‐polynomial that specializes to ().…”
Section: Log‐concavitymentioning
confidence: 57%
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“…By [21, Lemma 4.8], this is indeed a strengthening of the statement that () is denormalized Lorentzian. Even when D$\mathrm{D}$ has an enveloping matroid, we do not know if there is a denormalized Lorentzian evaluation of the multivariable U$U$‐polynomial that specializes to ().…”
Section: Log‐concavitymentioning
confidence: 57%
“…Proof To obtain the first two results, we set x=0,q=1$x = 0, q = 1$ in () and set x=y=0,w=1$x = y = 0, w=1$ in (), respectively, and then apply [21, Lemma 4.8]. To obtain the last two results, we normalize () and set y=q=1/2,xi=u$y = q = 1/2, x_i = u$ and set y=0,q=1,xi=u$y = 0, q = 1, x_i = u$, respectively, and then apply [20, Corollary 3.7].$\Box$…”
Section: Log‐concavitymentioning
confidence: 99%
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“…Lorentzian polynomials satisfy a strong log-concavity property [BH20, Example 2.26] and are preserved under nonnegative linear change of variables [BH20, Theorem 2.10]. Polynomials whose normalization is Lorentzian, called denormalized Lorentzian polynomials , share similar properties [BLP23, §4.3].…”
Section: Numerical Propertiesmentioning
confidence: 99%
“…For any rank r matroid on E , the polynomial is a denormalized Lorentzian polynomial in the sense of [BH20; BLP23].…”
Section: Introductionmentioning
confidence: 99%