An O(n)-randomness perturbation combining a dense deterministic pattern matrix with a non-uniform sparse dependent perturbation reduces condition numbers to O(n) for any input matrix.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
Recursive polynomial expansion for the matrix step function uses degree-eight components evaluated in three matrix multiplications to reduce overall multiplication count versus prior recursive methods.
Deterministic sketching via row subset selection produces subspace embeddings with probability 1 for Krylov methods and yields performance comparable to randomized sketching for matrix functions, linear systems, and eigenvalue problems.
Iteris, an agentic research system, produced evidence and drafts for two open computational math problems that were verified after human correction.
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
citing papers explorer
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Well-Conditioned Oblivious Perturbations in Linear Space
An O(n)-randomness perturbation combining a dense deterministic pattern matrix with a non-uniform sparse dependent perturbation reduces condition numbers to O(n) for any input matrix.
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Recursive expansion of the matrix step function using polynomials of degree eight
Recursive polynomial expansion for the matrix step function uses degree-eight components evaluated in three matrix multiplications to reduce overall multiplication count versus prior recursive methods.
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Deterministic sketching for Krylov subspace methods
Deterministic sketching via row subset selection produces subspace embeddings with probability 1 for Krylov methods and yields performance comparable to randomized sketching for matrix functions, linear systems, and eigenvalue problems.
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Iteris: Agentic Research Loops for Computational Mathematics
Iteris, an agentic research system, produced evidence and drafts for two open computational math problems that were verified after human correction.
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On subspace-constrained preconditioning for randomized iterative methods
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
- Oblivious Subspace Injection Is Not Enough for Relative Error