TRIQS/SOM: Implementation of the stochastic optimization method for analytic continuation

Abstract We present the TRIQS / SOM analytic continuation package, an efficient implementation of the Stochastic Optimization Method proposed by Mishchenko et al. (2000). TRIQS / SOM strives to provide a high quality open source (distributed under the GNU General Public License version 3) alternative to the more widely adopted Maximum Entropy continuation programs. It supports a variety of analytic continuation problems encountered in the field of computational condensed matter physics. Those problems can be formulated in terms of response functions of imaginary time, Matsubara frequencies or in the Legendre polynomial basis representation. The application is based on the TRIQS C++/Python framework, which allows for easy interoperability with TRIQS -based quantum impurity solvers, electronic band structure codes and visualization tools. Similar to other TRIQS packages, it comes with a convenient Python interface. Program summary Program Title: TRIQS / SOM Program Files doi: http://dx.doi.org/10.17632/fcjzyhrwpw.1 Licensing provisions: GNU General Public License (GPLv3) Programming language: C++ / Python External routines/libraries: TRIQS 1.4.2 , Boost >=1.58 , cmake . Nature of problem: Quantum Monte Carlo methods (QMC) are powerful numerical techniques widely used to study quantum many-body effects and electronic structure of correlated materials. Obtaining physically relevant spectral functions from noisy QMC  results in the imaginary time/Matsubara frequency domains requires solution of an ill-posed analytic continuation problem as a post-processing step. Solution method: We present an efficient C++/Python open-source implementation of the stochastic optimization method for analytic continuation. Additional comments: Project homepage http://krivenko.github.io/som/ .