1994
DOI: 10.1016/0370-2693(94)90440-5
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Stochastic estimation with Z2 noise

Abstract: We introduce a Z 2 noise for the stochastic estimation of matrix inversion and discuss its superiority over other noises including the Gaussian noise. This algorithm is applied to the calculation of quark loops in lattice quantum chromodynamics that involves diagonal and off-diagonal traces of the inverse matrix. We will point out its usefulness in its applications to estimating determinants, eigenvalues, and eigenvectors, as well as its limitations based on the structure of the inverse matrix.

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Cited by 175 publications
(153 citation statements)
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“…More recently, applications to three-point functions have shown their effectiveness for form factor calculations [7,9]. We consider the use of Z(2) ⊗ Z (2) random noisy source as introduced in [17].…”
Section: B Choice Of Momenta Carried By Quarksmentioning
confidence: 99%
See 1 more Smart Citation
“…More recently, applications to three-point functions have shown their effectiveness for form factor calculations [7,9]. We consider the use of Z(2) ⊗ Z (2) random noisy source as introduced in [17].…”
Section: B Choice Of Momenta Carried By Quarksmentioning
confidence: 99%
“…2 we present our method to calculate the pion form factor. In order to access the region of small momentum transfer, we use the method of partially twisted boundary condition [11][12][13][14], and in order to fight increasing computational cost for smaller pion mass, we apply the method of random wall source [9,[15][16][17][18][19]. In addition we make use of a judicious choice of momenta carried by the incoming and outgoing quarks off the electromagnetic vertex, which helps in reducing statistical fluctuations in the form factor measurements.…”
Section: Introductionmentioning
confidence: 99%
“…The number of low-modes is set to N e = 160. The contribution of the remaining high-modes is estimated by the noise method [8]. For each configuration, we prepare a complex Z 2 noise vector η(x), which is diluted into…”
Section: Pos(lattice 2015)121mentioning
confidence: 99%
“…To reduce its variance, stochastic volume sources (SVS) [11] and dilution [12] techniques are used to estimate S h stochastically. That is, low-mode averaging is combined with stochastic sources (LMA + SVS).…”
Section: Pos(lattice 2012)131mentioning
confidence: 99%