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      A \(N\)-Body Solver for Square Root Iteration

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          Abstract

          We develop the Sparse Approximate Matrix Multiply (\(\tt SpAMM\)) \(n\)-body solver for first order Newton Schulz iteration of the matrix square root and inverse square root. The solver performs recursive two-sided metric queries on a modified Cauchy-Schwarz criterion, culling negligible sub-volumes of the product-tensor for problems with structured decay in the sub-space metric. These sub-structures are shown to bound the relative error in the matrix-matrix product, and in favorable cases, to enjoy a reduced computational complexity governed by dimensionality reduction of the product volume. A main contribution is demonstration of a new, algebraic locality that develops under contractive identity iteration, with collapse of the metric-subspace onto the identity's plane diagonal, resulting in a stronger \(\tt SpAMM\) bound. Also, we carry out a first order {Fr\'{e}chet} analyses for single and dual channel instances of the square root iteration, and look at bifurcations due to ill-conditioning and a too aggressive \(\tt SpAMM\) approximation. Then, we show that extreme \(\tt SpAMM\) approximation and contractive identity iteration can be achieved for ill-conditioned systems through regularization, and we demonstrate the potential for acceleration with a scoping, product representation of the inverse factor.

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          Basis sets are some of the most important input data for computational models in the chemistry, materials, biology, and other science domains that utilize computational quantum mechanics methods. Providing a shared, Web-accessible environment where researchers can not only download basis sets in their required format but browse the data, contribute new basis sets, and ultimately curate and manage the data as a community will facilitate growth of this resource and encourage sharing both data and knowledge. We describe the Basis Set Exchange (BSE), a Web portal that provides advanced browsing and download capabilities, facilities for contributing basis set data, and an environment that incorporates tools to foster development and interaction of communities. The BSE leverages and enables continued development of the basis set library originally assembled at the Environmental Molecular Sciences Laboratory.
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            Gaussian elimination is not optimal

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              Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication

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                Author and article information

                Journal
                2015-08-24
                2015-10-13
                Article
                1508.05856
                8f747433-1746-4400-85ef-820c09b5ca49

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                LA-UR-15-26304
                cs.NA math.NA

                Numerical & Computational mathematics
                Numerical & Computational mathematics

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