performance - Speeding up an Euler numerical scheme in python -


let's have update scheme looks this:

import numpy np  n = 1000 dt = 0.01  x = np.zeros(n) x[0] = 0.5  in xrange(1, n):      rand = np.random.normal(loc=0.,scale=1.)      x[i] = x[i-1]*(1 + dt + np.sqrt(dt)*rand)

what best strategy speeding code of form, current array element needs previous array element make calculation?

i attempting put vectorised form i'm bit stuck how use trailing array element update current one.

if there better solutions don't involve vectorisation i'm open too.

try this:

x = np.random.randn(1000) x += 1 x[0] = 0.5 y = np.cumprod(x)

answer in y


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