Abstract
The problem of minimizing the number of zero elements that become non-zero during the computation, when a large sparse matrix is reduced to the Hessenberg (almost triangular) form by Gaussian similarity transformations, is discussed. Algorithms for minimizing the growth of such non-zero elements are given.
*This research was supported in part by the National Aeronautics and Space Administration, Washington, D.C., Grant No. NGR-33-015-013.
*This research was supported in part by the National Aeronautics and Space Administration, Washington, D.C., Grant No. NGR-33-015-013.
Notes
*This research was supported in part by the National Aeronautics and Space Administration, Washington, D.C., Grant No. NGR-33-015-013.