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Creators/Authors contains: "Zhao, Shifan"

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  1. ; ; ; ; (, SIAM Journal on Scientific Computing)
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  2. ; ; ; ; (, SIAM Journal on Matrix Analysis and Applications)
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  3. ; ; ; ; ; ; (, ACM)
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  4. ; ; ; ; (, SIAM journal on matrix analysis and applications)
    This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm has strong similarities with Anderson acceleration as well as with inexact Newton methods—depending on which variant is implemented. We prove theoretically and verify experimentally, on a variety of problems from simulation experiments to deep learning applications, that our method is a powerful accelerated iterative algorithm. The code is available at https://github.com/Data-driven-numerical-methods/Nonlinear-Truncated-Conjugate-Residual. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, Association for Computational Linguistics)
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  6. ; ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; (, The Tenth International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, Computer Aided Geometric Design)
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