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Accelerating a restarted Krylov method for matrix functions with randomization
Many scientific applications require the evaluation of the action of a matrix function on a vector, and Krylov subspace methods are the most common ones for this task. Since the orthogonalization cost and the memory requirements can quickly become overwhelming as the basis grows, the Krylov method i
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