When solving the linear system ${\bf A}x = {\bf b}$, the condition number $K(A) \equiv \| A \| \| A^{ - 1} \|$ is a useful, albeit often overly conservative, measure of the sensitivity of the solution ${\bf x}$ under...
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When solving the linear system ${\bf A}x = {\bf b}$, the condition number $K(A) \equiv \| A \| \| A^{ - 1} \|$ is a useful, albeit often overly conservative, measure of the sensitivity of the solution ${\bf x}$ under perturbations $\Delta A$ and $\Delta {\bf b}$ to A and ${\bf b}$. We demonstrate how the projection of ${\bf b}$ onto the range space of A, in addition to $K(A)$, can strongly affect the sensitivity of ${\bf x}$ in specific problem instances. Two practical cases are presented in which the sensitivity of ${\bf x}$ can be substantially smaller than that predicted by $K(A)$ alone. In the first example, we characterize a class of Vandermonde matrices and right-hand sides for which accurate algorithms can exist. For the second example, we show that a (fast Fourier transform-) FFT-based fast Poisson solver can produce very accurate results for smooth right-hand sides. Computational examples on the fast Poisson solver are included to illustrate these concepts.
A practical algorithm for solving the linear complementarity problem [w — Mz = q, w>0. z >0. wz=0] is presented. This algorithm is based on the n-cycle algorithm, which is known to converge if M is a nondegener...
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The effect of rounding errors on an algebraic process is often investigated by means of a so-called backward analysis. In this paper we will discuss the possibility of performing such an analysis on a computer. We beg...
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The effect of rounding errors on an algebraic process is often investigated by means of a so-called backward analysis. In this paper we will discuss the possibility of performing such an analysis on a computer. We begin with a precise definition of a stable algorithm, i.e., an algorithm which is relatively insensitive to rounding errors.
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