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   Tait, Alexander N.; Wu, Allie X.; Ferreira de Lima, Thomas; Nahmias, Mitchell A.; Shastri, Bhavin J.; Prucnal, Paul R. (May 2018, Optics Letters)
2. POLE: polarized embedding for signed networks

   Z. Huang; A. Silva; A. K. Singh (January 2022, WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining)
3. Minimal pole representation for spectral functions

   https://doi.org/10.1063/5.0273763  

   Zhang, Lei; Erpenbeck, André; Yu, Yang; Gull, Emanuel (June 2025, The Journal of Chemical Physics)

   Representing spectral densities, real-frequency, and real-time Green’s functions of continuous systems by a small discrete set of complex poles is a ubiquitous problem in condensed matter physics, with applications ranging from quantum transport simulations to the simulation of strongly correlated electron systems. This paper introduces a method for obtaining a compact, approximate representation of these functions, based on their parameterization on the real axis and a given approximate precision. We show applications to typical spectral functions and results for structured and unstructured correlation functions of model systems. 

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4. Minimum-gain Pole Placement with Sparse Static Feedback

   https://doi.org/10.1109/TAC.2020.3018615  

   Katewa, Vaibhav; Pasqualetti, Fabio (July 2020, IEEE Transactions on Automatic Control)

   The minimum-gain eigenvalue assignment/pole placement problem (MGEAP) is a classical problem in LTI systems with static state feedback. In this paper, we study the MGEAP when the state feedback has arbitrary sparsity constraints. We formulate the sparse MGEAP problem as an equality-constrained optimization problem and present an analytical characterization of its locally optimal solution in terms of eigenvector matrices of the closed loop system. This result is used to provide a geometric interpretation of the solution of the non-sparse MGEAP, thereby providing additional insights for this classical problem. Further, we develop an iterative projected gradient descent algorithm to obtain local solutions for the sparse MGEAP using a parametrization based on the Sylvester equation. We present a heuristic algorithm to compute the projections, which also provides a novel method to solve the sparse EAP. Also, a relaxed version of the sparse MGEAP is presented and an algorithm is developed to obtain approximately sparse local solutions to the MGEAP. Finally, numerical studies are presented to compare the properties of the algorithms, which suggest that the proposed projec 

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5. Pole Skipping in Holographic Theories with Bosonic Fields

   https://doi.org/10.1103/PhysRevLett.129.231603  

   Wang, Diandian; Wang, Zi-Yue (November 2022, Physical Review Letters)