randomized algorithm

Note that this distinction is only meaningful for randomized algorithms.

For general weights, there are randomized algorithms whose expected running time is linear.

This program works by using a partially randomized algorithm.

Randomized algorithms can be used in solving game trees.

A randomized algorithm makes a set of random choices.

For instance, a randomized algorithm may be prepared to handle some number of collisions.

This is an instance of a randomized algorithm.

In some cases, such randomized algorithms are able to outperform the best deterministic methods.

A well-designed randomized algorithm will have a very high probability of returning a correct answer.

However, the randomized algorithm must still output the correct answer for all inputs: it is not allowed to make errors.

In the setting with many satisfying assignments the randomized algorithm by Schöning has a better bound.

Moreover, the randomized algorithm described above resamples an event at most an expected times before it finds such an evaluation.

Therefore, utilization of randomized algorithms can speed up a very slow deterministic algorithm.

The actions of the trusted server are modeled via a randomized algorithm .

The fastest randomized algorithms employ the multi-trials technique by Schneider et al.

In many cases, the space requirements for an algorithm can be drastically cut by using a randomized algorithm.

This can be useful for randomized algorithms.

Despite the similarity in their names, stochastic computing is distinct from the study of randomized algorithms.

Competitive analysis is a way of doing worst case analysis for on-line and randomized algorithms, which are typically data dependent.

The words of Goethe proved prophetic, when in the 20th century randomized algorithms were discovered as powerful tools.

Consider the following simple randomized algorithm :

No randomized algorithm can do better.

Algorithms that use random bits are called randomized algorithms.

Even a randomized algorithm must make nearly as many queries in order to be confident of distinguishing these two matroids.

Observe that, even if is close to 1, are no longer independent random variables, which is often a problem in the analysis of randomized algorithms.