Separately, game theory has played a role in online algorithms.

In computer science, an online algorithm measures its competitiveness against different adversary models.

More formally, an online algorithm proceeds in a sequence of trials.

He is known for his work in cryptography, online algorithms, and algorithmic game theory.

Analysis of the paging problem has also been done in the field of online algorithms.

The following are the names of some online algorithms:

Many online algorithms can give strong guarantees on performance even when the instances are not generated by a distribution.

However, there are many situations (such as online algorithms) where this randomization is not viable.

The tight span serves a role in several online algorithms for the K-server problem.

Also, it was proved that List scheduling is optimum online algorithm for 2 and 3 machines.

For randomized online algorithms competitiveness can depend upon the adversary model used.

He is known for his work competitive analysis of online algorithms, particularly for the k-server problem.

When the step detection must be performed as and when the data arrives, then online algorithms are usually used.

Task systems are mathematical objects used to model the set of possible configuration of online algorithms.

An example of the online algorithm for kurtosis implemented as described is:

Efficiency of randomized online algorithms for the paging problem is measured using amortized analysis.

However, if the training set is not linearly separable, the above online algorithm will not converge.

For deterministic online algorithms, there is a tight bound on the competitive ratio due to Borodin et al. (1992).

He also pioneered the theory of link grammars and developed the technique of competitive analysis for online algorithms.

For example, the instances could describe the current conditions of the stock market, and an online algorithm predicts tomorrow's value of a particular stock.

As long as a reasonably good classifier exists, the online algorithm will learn to predict correct labels.

Online algorithms commonly use amortized analysis.

The learning algorithm for perceptrons is an online algorithm, in that it processes elements in the training set one at a time.

A problem exemplifying the concepts of online algorithms is the Canadian Traveller Problem.

Competitive analysis (online algorithm)