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arXiv · 2107.13265

Learned Optimizers for Analytic Continuation

Dongchen Huang·Yi-feng Yang
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Abstract

Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for parameter tuning. Here we propose a neural network method by convex optimization and replace the ill-posed inverse problem by a sequence of well-conditioned surrogate problems. After training, the learned optimizers are able to give a solution of high quality with low time cost and achieve higher parameter efficiency than heuristic fully-connected networks. The output can also be used as a neural default model to improve the maximum entropy for better performance. Our methods may be easily extended to other high-dimensional inverse problems via large-scale pretraining.

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BibTeXRIS

Dongchen Huang, Yi-feng Yang. 2021-07-28. Learned Optimizers for Analytic Continuation. https://doi.org/10.1103/physrevb.105.075112

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