Titlebook: Supercomputational Science; R. G. Evans,S. Wilson Book 1990 Plenum Press, New York 1990 Approximation.algorithms.dynamics.supercomputer

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D. J. Baker,S. Wilson,D. Moncrieffstency is lost if ., but that . still bears some information on . when . converges to a positive constant. When . diverges to infinity, we provide asymptotic confidence zones for . based on .. Our non-standard asymptotic results are supported by Monte Carlo exercises.

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Peter J. Knowlesean of the squares of the M-scales of the reconstruction errors of the S-ODPC converges to the mean of the squares of the M-scales of the idiosyncratic terms, with rate ., where . is the number of dimensions. A Monte Carlo study shows that the S-ODPC introduced in this chapter can be successfully us

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https://doi.org/10.1007/978-1-4684-5820-6Approximation; algorithms; dynamics; supercomputer

ARBOR

Good Programming Techniques I: Testing and the Life Cycle of a Software Product,These lectures are not about routine techniques such as Top—down—design and structured programming. They assume some knowledge of these techniques and attempt to build up testing and procedures which supplement these techniques.

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Numerical Recipes for Supercomputers,From the earliest days of the computer era, state—of—the—art scientific calculations have almost always demanded efficient computer programs. But in the present decade we have seen the advent of general purpose computing machines which perform parts of a calculation concurrently.

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