Jarzynski’s Equality, Fluctuation Theorems, and Variance Reduction: Mathematical Analysis and Numerical Algorithms
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Physics"
DOI: 10.1007/s10955-019-02286-4
Abstract: In this paper, we study Jarzynski’s equality and fluctuation theorems for diffusion processes. While some of the results considered in the current work are known in the (mainly physics) literature, we review and generalize these… read more here.
Keywords: jarzynski equality; fluctuation theorems; equality fluctuation; variance reduction ... See more keywords
Population-based variance reduction for dynamic Monte Carlo
Sign Up to like & getrecommendations! Published in 2020 at "Annals of Nuclear Energy"
DOI: 10.1016/j.anucene.2020.107752
Abstract: Abstract Dynamic Monte Carlo (DMC) simulation of realistic nuclear reactors requires powerful variance reduction methods for even a few seconds of real time calculations. State-of-the-art numerical methods deal with the dynamic nature of the problem… read more here.
Keywords: variance reduction; monte carlo; population; variance ... See more keywords
Adaptive importance sampling and control variates
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Mathematical Analysis and Applications"
DOI: 10.1016/j.jmaa.2019.123608
Abstract: Abstract We construct and investigate an adaptive variance reduction framework in which both importance sampling and control variates are employed. The three lines (Monte Carlo averaging and two variance reduction parameter search lines) run in… read more here.
Keywords: control variates; variance; importance sampling; variance reduction ... See more keywords
Generational Variance Reduction in Monte Carlo Criticality Simulations as a Way of Mitigating Unwanted Correlations
Sign Up to like & getrecommendations! Published in 2023 at "Nuclear Science and Engineering"
DOI: 10.1080/00295639.2023.2193089
Abstract: Monte Carlo criticality simulations are widely used in nuclear safety demonstrations, as they offer an arbitrarily precise estimation of global and local tallies while making very few assumptions. However, since the inception of such numerical… read more here.
Keywords: criticality; variance reduction; carlo criticality; monte carlo ... See more keywords
Stochastic distributed learning with gradient quantization and double-variance reduction
Sign Up to like & getrecommendations! Published in 2022 at "Optimization Methods and Software"
DOI: 10.1080/10556788.2022.2117355
Abstract: ABSTRACT We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model… read more here.
Keywords: stochastic distributed; variance reduction; distributed learning; compression ... See more keywords
A High-Accuracy Single-Photon Time-Interval Measurement in Mega-Hz Detection Rates With Collaborative Variance Reduction: Theoretical Analysis and Realization Methodology
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2022.3206406
Abstract: An almost all-digital time-to-digital converter possessing sub-picosecond resolution, scalable dynamic range, calibratable linearity, high noise-immunity, and fast conversion-rates can be achieved by a stochastic random sampling-and-averaging approach with the proposed collaborative variance reduction (VR) technique… read more here.
Keywords: time; collaborative variance; methodology; theoretical analysis ... See more keywords
Improved Variance Reduction Methods for Riemannian non-Convex Optimization.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3112139
Abstract: Variance reduction is popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both Euclidean space and Riemannian manifold. This paper further improves on existing variance reduction methods for non-convex Riemannian… read more here.
Keywords: optimization; variance; non convex; reduction methods ... See more keywords
Empirical variance minimization with applications in variance reduction and optimal control
Sign Up to like & getrecommendations! Published in 2022 at "Bernoulli"
DOI: 10.3150/21-bej1392
Abstract: We study the problem of empirical minimization for variance-type functionals over functional classes. Sharp non-asymptotic bounds for the excess variance are derived under mild conditions. In particular, it is shown that under some restrictions imposed… read more here.
Keywords: minimization; variance; optimal control; reduction optimal ... See more keywords