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- S. Chandrasekaran, S. Venkatesan
- J. Parallel Distrib. Comput.
- 1990

- S. Chandrasekaran, M. Gu, A. H. Sayed
- SIAM J. Matrix Analysis Applications
- 1998

We develop an algorithm for the solution of indefinite least-squares problems. Such problems arise in robust estimation, filtering, and control, and numerically stable solutions have been lacking.â€¦ (More)

- S. Chandrasekaran, Ilse C. F. Ipsen
- SIAM J. Matrix Analysis Applications
- 1995

Expressions are presented for the errors in individual components of the solution to systems of linear equations and linear least squares problems. No assumptions about the structure or distributionâ€¦ (More)

â€˜Hierarchical Semi-separableâ€™ matrices (HSS matrices) form an important class of structured matrices for which matrix transformation algorithms that are linear in the number of equations (and aâ€¦ (More)

- S. Chandrasekaran, Gene H. Golub, M. Gu, A. H. Sayed
- SIAM J. Matrix Analysis Applications
- 1999

We pose and solve a parameter estimation problem in the presence of bounded data uncertainties. The problem involves a minimization step and admits a closed form solution in terms of the positiveâ€¦ (More)

- Minghua Shi, Amine Bermak, S. Chandrasekaran, Abbes Amira, Sofiane Brahim-Belhouari
- IEEE Sensors Journal
- 2008

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improvedâ€¦ (More)

Let q â‰¥ 1 be an integer. Given M samples of a smooth function of q variables, 2Ï€â€“periodic in each variable, we consider the problem of constructing a qâ€“variate trigonometric polynomial of sphericalâ€¦ (More)

- S. Chandrasekaran, Ilse C. F. Ipsen
- SIAM J. Matrix Analysis Applications
- 1995

We extend the Golub-Kahan algorithm for computing the singular value decomposition of bidiagonal matrices to triangular matrices R. Our algorithm avoids the explicit formation of R T R or RRT. Weâ€¦ (More)

- S. Chandrasekaran
- SIAM J. Matrix Analysis Applications
- 2000

A new, efficient, and stable algorithm for computing all the eigenvalues and eigenvectors of the problem Ax = Î»Bx, where A is symmetric indefinite and B is symmetric positive definite, is proposed.

We present bounds on the backward errors for the symmetric eigen-value decomposition and the singular value decomposition in the two-norm and in the Frobenius norm. Through diierent orthogonalâ€¦ (More)