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Advice Complexity and Barely Random Algorithms

Dennis Komm, Richard Královič (2011)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

Recently, a new measurement – the advice complexity – was introduced for measuring the information content of online problems. The aim is to measure the bitwise information that online algorithms lack, causing them to perform worse than offline algorithms. Among a large number of problems, a well-known scheduling problem, job shop scheduling with unit length tasks, and the paging problem were analyzed within this model. We observe some connections between advice complexity and randomization. Our...

Advice Complexity and Barely Random Algorithms

Dennis Komm, Richard Královič (2011)

RAIRO - Theoretical Informatics and Applications

Recently, a new measurement – the advice complexity – was introduced for measuring the information content of online problems. The aim is to measure the bitwise information that online algorithms lack, causing them to perform worse than offline algorithms. Among a large number of problems, a well-known scheduling problem, job shop scheduling with unit length tasks, and the paging problem were analyzed within this model. We observe some connections between advice complexity and randomization....

Applying A Normalized Compression Metric To The Measurement Of Dialect Distance

Simov, Kiril, Osenova, Petya (2007)

Serdica Journal of Computing

The paper discusses the application of a similarity metric based on compression to the measurement of the distance among Bulgarian dia- lects. The similarity metric is de ned on the basis of the notion of Kolmo- gorov complexity of a le (or binary string). The application of Kolmogorov complexity in practice is not possible because its calculation over a le is an undecidable problem. Thus, the actual similarity metric is based on a real life compressor which only approximates the Kolmogorov complexity....

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