# 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