The Decline of the Left Wing in Israel

Shilon, Avi

doi:10.5040/9781838601133

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2021

2022

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Cited by 3 publications

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“…r = cp(A) if cp(A) is known, or r is sufficiently larger, otherwise. We used RandOrthMat.m [39] to generate a random starting point X 0 on the basis of the Gram-Schmidt process.…”

Section: Numerical Resultsmentioning

confidence: 99%

Completely Positive Factorization by a Riemannian Smoothing Method

Lai¹,

Yoshise²

2021

Preprint

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We examine the problem of completely positive (CP) factorization for a given completely positive matrix. We are inspired by the idea of Groetzner and Dür in 2020, wherein the CP factorization problem can be reformulated as an equivalent feasibility problem containing an orthogonality constraint. We begin by solving this feasibility problem through the use of a special case of the Riemannian smoothing steepest descent method proposed by Zhang et al. in 2021. To apply it to the CP factorization problem, we employ a smooth approximation function, named LogSumExp, as the maximum function. Numerical experiments show the efficiency of our method especially for large-scale factorizations.

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“…r = cp(A) if cp(A) is known, or r is sufficiently larger, otherwise. We used RandOrthMat.m [39] to generate a random starting point X 0 on the basis of the Gram-Schmidt process.…”

Section: Numerical Resultsmentioning

confidence: 99%