Introduces OSA-IOP and OSLQ as scalable stochastic Krylov methods for large-scale log-determinant estimation, with derived error bounds and numerical experiments on real-world matrices.
Suboptimal subspace con- struction for log-determinant approximation.arXiv preprint arXiv:2307.02152, 2023
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Introduces an optimized error reallocation for stochastic Lanczos quadrature that minimizes total matrix-vector multiplications by allocating more budget to the Lanczos process than to Monte Carlo sampling for a target accuracy.
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Optimal Stochastic Krylov based Techniques for Large- Scale Log-Determinant Estimation
Introduces OSA-IOP and OSLQ as scalable stochastic Krylov methods for large-scale log-determinant estimation, with derived error bounds and numerical experiments on real-world matrices.
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An analysis on stochastic Lanczos quadrature with asymmetric quadrature nodes
Introduces an optimized error reallocation for stochastic Lanczos quadrature that minimizes total matrix-vector multiplications by allocating more budget to the Lanczos process than to Monte Carlo sampling for a target accuracy.