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Fundamentals of matrix computations
David S. Watkins
- 01 Jan 1991
TL;DR: This paper focuses on Gaussian Elimination as a model for Iterative Methods for Linear Systems, and its applications to Singular Value Decomposition and Sparse Eigenvalue Problems.
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Abstract: Gaussian Elimination and its Variants Sensitivity of Linear Systems Effects of Roundoff Errors Orthogonal Matrices and the Least Squares Problem Eigenvalues, Eigenvectors and Invariant Subspaces Other Methods for the Symmetric Eigenvalue Problem The Singular Value Decomposition Appendices Bibliography
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Citations
A new rosette design for more reliable hole-drilling residual stress measurements
Gary S. Schajer,M. Tootoonian +1 more
TL;DR: In this article, a six-element strain gage rosette is presented that can greatly improve residual stress measurement accuracy when using the hole-drilling method and can identify stresses at one-third greater depths from the measurement surface.
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Randomized load balancing for tree-structured computation
Soumen Chakrabarti,Abhiram Ranade,Katherine Yelick +2 more
- 23 May 1994
TL;DR: This work shows that the time taken to explore a task tree is likely to be within a small constant factor of an inherent lower bound for the tree instance, and thus extends earlier work which assumed fixed cost tasks and used a bulk synchronous style.
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On Plug-and-Play Regularization Using Linear Denoisers
TL;DR: In this article, it was shown that a broader class of linear denoisers can be expressed as a proximal map of some convex regularizer, such as BM3D or CNN denoiser.
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On the stability of the FDTD algorithm for elastic media at a material interface
C.T. Schroder,Waymond R. Scott +1 more
TL;DR: It is shown that this more restrictive stability condition of the first-order finite-difference time-domain algorithm for elastodynamics at the interface between two different materials can be avoided if the material properties are averaged on the boundary.
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