This paper presents a preconditioning method based on combining two-sided permutations with a multilevel approach. The nonsymmetric permutation exploits a greedy strategy to put large entries of the matrix in the diagonal of the upper leading submatrix. The method can be regarded as a complete pivoting version of the incomplete LU factorization. This leads to an effective incomplete factorizati...

A real square matrix A is said to be Lyapunov diagonally semistable if there exists a positive definite diagonal matrix D, called a Lyapunov scaling factor of A, such that the matrix AD + DAT is positive semidefinite, Lyapunov diagonally semistable matrices play an important role in applications in several disciplines, and have been studied in many matrix theoretical papers, see for example [2]...

We give upper and lower bounds on the determinant of a perturbation of the identity matrix or, more generally, a perturbation of a nonsingular diagonal matrix. The matrices considered are, in general, diagonally dominant. The lower bounds are best possible, and in several cases they are stronger than well-known bounds due to Ostrowski and other authors. If A = I−E is a real n×n matrix and the e...

This paper proposes a method to simulate inextensible hair strands using tridiagonal matrix formulation in which distance constraints are formulated as a linear system. The proposed method avoids constructing a full matrix explicitly. Instead, it takes advantage of the chain topology and serial indexing to formulate symmetric tridiagonal matrix. Furthermore, we use a linear distance constraint ...

We continue the comparison of parallel algorithms for solving diagonally dominant and general narrow-banded linear systems of equations that we started in [2]. The solvers compared are the banded system solvers of ScaLAPACK [6] and those investigated by Arbenz and Hegland [1, 5]. We present the numerical experiments that we conducted on the IBM SP/2.

We present a linear work parallel iterative algorithm for solving linear systems involving Laplacians of planar graphs. In particular, if Ax = b, where A is the Laplacian of any planar graph with n nodes, the algorithm produces a vector x̄ such that ||x − x̄||A ≤ ǫ, in O(n log(1/ǫ)) parallel time, doing O(n log(1/ǫ)) work, where c is any positive constant. One of the key ingredients of the solver...