scholarly journals Maximal variance reduction for stochastic propagators with applications to the static quark spectrum

1998 ◽  
Vol 58 (3) ◽  
Author(s):  
C. Michael ◽  
J. Peisa
Keyword(s):  
Variance Reduction ◽  
Static Quark

Variance Reduction for Monte Carlo Methods

2018 ◽  
Vol 482 (6) ◽  
pp. 627-630
Author(s):  
D. Belomestny ◽  
◽  
L. Iosipoi ◽  
N. Zhivotovskiy ◽  
◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Variance Reduction

Accelerating Monte Carlo: quasirandom sequences and variance reduction

1997 ◽  
Vol 1 (2) ◽  
pp. 79-95 ◽  
Author(s):  
Leonard Berman
Keyword(s):  
Monte Carlo ◽  
Variance Reduction

Variance reduction methods applied to deep-penetration Monte Carlo problems

1986 ◽  
Author(s):  
S N Cramer ◽  
J S Tang
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Deep Penetration ◽  
Reduction Methods

scholarly journals Determination of α(Mz) from an hyperasymptotic approximation to the energy of a static quark-antiquark pair

2020 ◽  
Vol 2020 (9) ◽  
Author(s):  
Cesar Ayala ◽  
Xabier Lobregat ◽  
Antonio Pineda
Keyword(s):  
Perturbation Theory ◽  
Energy Range ◽  
Short Distance ◽  
Weak Coupling ◽  
Strong Consistency ◽  
Lattice Data ◽  
Static Potential ◽  
Static Energy ◽  
Static Quark

Abstract We give the hyperasymptotic expansion of the energy of a static quark-antiquark pair with a precision that includes the effects of the subleading renormalon. The terminants associated to the first and second renormalon are incorporated in the analysis when necessary. In particular, we determine the normalization of the leading renormalon of the force and, consequently, of the subleading renormalon of the static potential. We obtain $$ {Z}_3^F $$ Z 3 F (nf = 3) = $$ 2{Z}_3^V $$ 2 Z 3 V (nf = 3) = 0.37(17). The precision we reach in strict perturbation theory is next-to-next-to-next-to-leading logarithmic resummed order both for the static potential and for the force. We find that the resummation of large logarithms and the inclusion of the leading terminants associated to the renormalons are compulsory to get accurate determinations of $$ {\Lambda}_{\overline{\mathrm{MS}}} $$ Λ MS ¯ when fitting to short-distance lattice data of the static energy. We obtain $$ {\Lambda}_{\overline{\mathrm{MS}}}^{\left({n}_f=3\right)} $$ Λ MS ¯ n f = 3 = 338(12) MeV and α(Mz) = 0.1181(9). We have also MS found strong consistency checks that the ultrasoft correction to the static energy can be computed at weak coupling in the energy range we have studied.

Variance Reduction by Antithetic Variates inGI/G/1 Queuing Simulations

1973 ◽  
Vol 21 (4) ◽  
pp. 988-997 ◽  
Author(s):  
Bill Mitchell
Keyword(s):  
Variance Reduction ◽  
Antithetic Variates

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

scholarly journals Variance reduction for sequential sampling in stochastic programming

2021 ◽  
Author(s):  
Jangho Park ◽  
Rebecca Stockbridge ◽  
Güzin Bayraksan
Keyword(s):  
Stochastic Programming ◽  
Variance Reduction ◽  
Sequential Sampling
Sign in / Sign up

Export Citation Format

Share Document