Subspace iterations are used to minimise a generalised Ritz functional of a large, sparse Hermitean matrix. In this way, the lowest m eigenvalues are determined. Tests with 1 less than or equal to m less than or equal to 32 demonstrate that the computational cost (no. of matrix multiplies) does not increase substantially with m. This implies that, as compared to the case of a m=1, the additional eigenvalues are obtained for free. [References: 5]
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