ACM Transactions on Mathematical Software (TOMS)
The Decompositional Approach to Matrix Computation
Computing in Science and Engineering
Polynomial Preconditioning for Specially Structured Linear Systems of Equations
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Enclosing clusters of zeros of polynomials
Journal of Computational and Applied Mathematics
Clustered Blockwise PCA for Representing Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new singular value decomposition algorithm suited to parallelization and preliminary results
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Reducing the space-time complexity of the CMA-ES
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fast algorithms for spherical harmonic expansions, II
Journal of Computational Physics
Parallel block tridiagonalization of real symmetric matrices
Journal of Parallel and Distributed Computing
Algorithm 880: A testing infrastructure for symmetric tridiagonal eigensolvers
ACM Transactions on Mathematical Software (TOMS)
Sequential optimal design of neurophysiology experiments
Neural Computation
A new algorithm for singular value decomposition and its parallelization
Parallel Computing
Parallelization of divide-and-conquer eigenvector accumulation
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
A matrix hyperbolic cosine algorithm and applications
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Mirror descent for metric learning: a unified approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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An algorithm is presented for computing the eigendecomposition of a symmetric rank-one modification of a symmetric matrix whose eigendecomposition is known. Previous algorithms for this problem suffer a potential loss of orthogonality among the computed eigenvectors, unless extended precision arithmetic is used. This algorithm is based on a novel, stable method for computing the eigenvectors. It does not require extended precision and is as efficient as previous approaches.