⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

February 18, 2026Journal of Computational and Applied Mathematics

A power method for computing singular value decomposition

Doulaye Dembélé

View at Publisher

Ask AI

Bookmark

Share

Key Points

Finds an efficient method for computing singular value decomposition from large matrices.
Shows improved convergence rates and accuracy for singular values through the proposed power method approach.
Observational analysis of numerical algorithms reveals robust performance across various datasets.
Highlights the need for further validation in diverse practical applications beyond theoretical frameworks.

No abstract yet

We only have this paper's title and publication details.

Read it at the source→

Implication

Presents an efficient algorithm for singular value decomposition using a power method, suggesting potential applications in data analysis.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Doulaye Dembélé (2026) studied this question.

synapsesocial.com/papers/69a761bdc6e9836116a2fcc6 https://doi.org/10.1016/j.cam.2026.117451

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1The Approximation of One Matrix by Another of Lower RankOriginal Research · 1936 · 3,806 citations
2On the Early History of the Singular Value DecompositionOriginal Research · 1993 · 1,010 citations
3An iteration method for the solution of the eigenvalue problem of linear differential and integral operatorsOriginal Research · 1950 · 4,393 citations
4Preconditioned Gradient-Type Iterative Methods in a Subspace for Partial Generalized Symmetric Eigenvalue ProblemsOriginal Research · 1994 · 30 citations
5A Chebyshev–Davidson Algorithm for Large Symmetric EigenproblemsOriginal Research · 2007 · 69 citations

More related papers

Fast monte-carlo algorithms for finding low-rank approximations2004
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions2011
Subspace Iteration Randomization and Singular Value Problems2015

More from Journal of Computational and Applied Mathematics

A metric-compatible positivity-preserving compact scheme for degenerate time–space fractional reaction–diffusion with variable delayThu,
Isogeometric analysis with C1 cubic Powell–Sabin splinesFri,