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Embedding Methods Proposed for Linear Solver Performance

Modernity/arxiv 2h1h Impact 5
Researchers Hayden Liu Weng, Hans-Joachim Bungartz, and Felix Dietrich have proposed new embedding-based methods for predicting the performance of linear solvers. These methods aim to address the computational effort dominated by solving large, sparse linear systems in scientific applications. New embedding methods have been proposed to improve the performance of linear solvers. These techniques could enhance computational efficiency in various scientific and engineering applications.

Topics

linear solvers computational science machine learning

Developing

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Sources · 7 independent

Modernity/arxiv

“Embedding-based Methods for Linear Solver Performance Prediction. Authors: Hayden Liu Weng, Hans-Joachim Bungartz, Felix Dietrich Abstract: The solution of large, sparse linear systems often dominates the computational effort of scientific applications and is a fre...”

arxiv

“Olver Abstract: We show that the QR factorization of a banded-plus-semiseparable (BPS) matrix is computable in optimal linear complexity with respect to the discretization ...”

Modernity/arxiv

“Authors: Taisuke Kobayashi, Naoto Yamanaka Abstract: This paper proposes a novel regularization design to effectively smooth policy functions in reinforcement learning. While regularization that enha...”

Modernity/arxiv

“New embedding methods have been proposed to enhance the performance of linear solvers.”

BBC Radio 4

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