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1. Nevanlinna Analytical Continuation

   https://doi.org/10.1103/PhysRevLett.126.056402  

   Fei, Jiani; Yeh, Chia-Nan; Gull, Emanuel (February 2021, Physical Review Letters)

   null (Ed.)
2. TRIQS/Nevanlinna: Implementation of the Nevanlinna Analytic Continuation method for noise-free data

   https://doi.org/10.17632/4cbzfy5rds.1  

   Iskakov, Sergei; Hampel, Alexander; Wentzell, Nils; Gull, Emanuel (January 2024, Mendeley Data)

   We present the TRIQS/Nevanlinna analytic continuation package, an efficient implementation of the methods proposed by J. Fei et al. (2021) [53] and (2021) [55]. TRIQS/Nevanlinna 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 based analytic continuation programs. With the additional Hardy functions optimization procedure, it allows for an accurate resolution of wide band and sharp features in the spectral function. Those problems can be formulated in terms of imaginary time or Matsubara frequency response functions. The application is based on the TRIQS C++/Python framework, which allows for easy interoperability with other TRIQS-based applications, electronic band structure codes and visualization tools. Similar to other TRIQS packages, it comes with a convenient Python interface. 

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3. Nevanlinna representations in several variables

   https://doi.org/10.1016/j.jfa.2016.02.004  

   Agler, J.; Tully-Doyle, R.; Young, N.J. (April 2016, Journal of Functional Analysis)
4. A birational Nevanlinna constant and its consequences

   https://doi.org/10.1353/ajm.2020.0022  

   Ru, Min; Vojta, Paul (January 2020, American Journal of Mathematics)
5. Nevanlinna.jl: A Julia implementation of Nevanlinna analytic continuation

   https://doi.org/10.21468/SciPostPhysCodeb.19  

   Nogaki, Kosuke; Fei, Jiani; Gull, Emanuel; Shinaoka, Hiroshi (November 2023, SciPost Physics Codebases)

   We introduce a Julia implementation of the recently proposed Nevanlinna analytic continuation method. The method is based on Nevanlinna interpolants and inherently preserves the causality of a response function due to its construction. For theoretical calculations without statistical noise, this continuation method is a powerful tool to extract real-frequency information from numerical input data on the Matsubara axis. This method has been applied to first-principles calculations of correlated materials. This paper presents its efficient and full-featured open-source implementation of the method including the Hamburger moment problem and smoothing. 

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