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.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 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.
OSI alone does not yield relative-error guarantees for sketching; counterexamples for least-squares and randomized SVD show that upper control on the optimal residual is required to recover near-relative error.
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
-
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.
-
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.
-
Oblivious Subspace Injection Is Not Enough for Relative Error
OSI alone does not yield relative-error guarantees for sketching; counterexamples for least-squares and randomized SVD show that upper control on the optimal residual is required to recover near-relative error.
-
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.
-
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.
-
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.