{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/textttsom-implementation-of-the-stochastic","title":"$\\texttt{SOM}$: Implementation of the Stochastic Optimization Method for Analytic Continuation","arxiv_id":"1808.00603","date":"2018-08-02","proceeding":null,"authors":["Igor Krivenko","Malte Harland"],"abstract":"We present the $\\texttt{TRIQS}/\\texttt{SOM}$ analytic continuation package, an efficient implementation of the Stochastic Optimization Method proposed by A. Mishchenko et al [Phys. Rev. B $\\textbf{62}$, 6317 (2000)]. $\\texttt{TRIQS}/\\texttt{SOM}$ 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 $\\texttt{TRIQS}$ C++/Python framework, which allows for easy interoperability with $\\texttt{TRIQS}$-based quantum impurity solvers, electronic band structure codes and visualization tools. Similar to other $\\texttt{TRIQS}$ packages, it comes with a convenient Python interface.","url_abs":"http://arxiv.org/abs/1808.00603v1","url_pdf":"http://arxiv.org/pdf/1808.00603v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"textttsom-implementation-of-the-stochastic","repo_url":"https://github.com/krivenko/som","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}