estimator.rinse_and_repeat

estimator.rinse_and_repeat(f, n, alpha, q, success_probability=0.99, m=<Mock name='mock()' id='139938827128976'>, optimisation_target=u'red', decision=True, repeat_select=None, *args, **kwds)[source]

Find best trade-off between success probability and running time.

Parameters:
  • f – a function returning a cost estimate
  • n – LWE dimension n > 0
  • alpha – noise rate 0 ≤ α < 1, noise will have standard deviation αq/sqrt{2π}
  • q – modulus 0 < q
  • success_probability – targeted success probability < 1
  • optimisation_target – which value to minimise
  • decision – set if f solves a decision problem, unset for search problems
  • repeat_select – passed through to cost_repeat as parameter select
  • samples – the number of available samples