\texttt{TRIQS/SOM}: implementation of the stochastic optimization method for analytic continuation

DOI10.1016/J.CPC.2019.01.021arXiv1808.00603OpenAlexW2887052089MaRDI QIDQ6157668

Igor Krivenko, Malte Harland

Publication date: 12 May 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.00603

zbMATH Keywords

stochastic optimizationanalytic continuationquantum Monte Carlopython

Mathematics Subject Classification ID

Numerical analysis (65-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (5)

Analytic continuation from limited noisy Matsubara data ⋮ ACFlow: an open source toolkit for analytic continuation of quantum Monte Carlo data ⋮ ana\_cont: python package for analytic continuation ⋮ Progress on stochastic analytic continuation of quantum Monte Carlo data ⋮ Eigenmatrix for unstructured sparse recovery

Cites Work

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• A note on the Bessel polynomials
• Implementation of the maximum entropy method for analytic continuation
• A method for directly finding the denominator values of rational interpolants
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

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