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Search results with tag "Conjugate gradient"
A User's Guide to DMU
dmu.ghpc.au.dk2: Preconditioned Conjugate Gradient. scaling: 0: no scaling of data prior to computation = 1: data are scaled to unit residual variance before computations. Estimated parameters and effects are scaled back to the original units. test_prt = 0: Standard. Yield minimum amount of output 1: Standard output plus lists of all class levels and
Conjugate Gradient Method - Stanford University
stanford.edu• proposed by Hestenes and Stiefel in 1952 (as direct method) • solves SPD system Ax = b – in theory (i.e., exact arithmetic) in n iterations – each iteration requires a few inner products in Rn, and one matrix-vector multiply z → Az • for A dense, matrix-vector multiply z → Az costs n2, so total cost is n3, same as direct methods