Proves relative-gap-preserving error bounds for singular vectors and eigenvectors from mixed-precision Jacobi methods that depend on the preconditioned matrix scaled condition number rather than the original.
and Van Loan, Charles F
8 Pith papers cite this work, alongside 30,423 external citations. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 8representative citing papers
ALAC formulates accelerometer calibration as a constrained homogeneous least-squares problem on a combined error matrix, recovering scale, misalignment, and bias parameters from five attitude-aided measurements under static gravity.
Solar Orbiter data reveals SEP flux deflections associated with magnetic flux tubes, positioning SEPs as a diagnostic for solar wind structures.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.
The paper introduces matrix-multiplication-based iterative refinement for diagonalizable non-Hermitian eigendecompositions that achieves quadratic residual reduction for simple eigenvalues and includes cluster stabilization.
Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
citing papers explorer
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Computing accurate singular vectors and eigenvectors using mixed-precision Jacobi algorithms
Proves relative-gap-preserving error bounds for singular vectors and eigenvectors from mixed-precision Jacobi methods that depend on the preconditioned matrix scaled condition number rather than the original.
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Attitude-Aided Linear Calibration of Triaxial Accelerometers
ALAC formulates accelerometer calibration as a constrained homogeneous least-squares problem on a combined error matrix, recovering scale, misalignment, and bias parameters from five attitude-aided measurements under static gravity.
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Probing Solar Wind Structures with Solar Energetic Particle Observations from Solar Orbiter
Solar Orbiter data reveals SEP flux deflections associated with magnetic flux tubes, positioning SEPs as a diagnostic for solar wind structures.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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Matrix-Free 3D SIMP Topology Optimization with Fused Gather-GEMM-Scatter Kernels
A fused gather-GEMM-scatter CUDA kernel achieves 4.6-7.3x end-to-end speedup and 3.2-4.9x lower energy for matrix-free 3D SIMP topology optimization on RTX 4090 compared to three-stage baselines.
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Iterative Refinement for Diagonalizable Non-Hermitian Eigendecompositions
The paper introduces matrix-multiplication-based iterative refinement for diagonalizable non-Hermitian eigendecompositions that achieves quadratic residual reduction for simple eigenvalues and includes cluster stabilization.
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Generalizing Reduced Rank Extrapolation to Low-Rank Matrix Sequences
Two generalizations of reduced rank extrapolation are derived for low-rank matrix sequences and iteration-dependent mapping functions, with numerical tests on Lyapunov and Riccati equations.
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A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.