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Advice Complexity: Quantitative Approach to A-Priori Information

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SOFSEM 2014: Theory and Practice of Computer Science (SOFSEM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8327))

Abstract

We survey recent results from different areas, studying how introducing per-instance a-priori information affects the solvability and complexity of given tasks. We mainly focus on distributed, and online computation, where some sort of hidden information plays a crucial role: in the distributed computing, typically nodes have no or only limited information about the global state of the network; in online problems, the algorithm lacks the information about the future input. The traditional approach in both areas is to study how the properties of the problem change if some partial information is available (e.g., nodes of a distributed system have sense of direction, the online algorithm has the promise that the input requests come in some specified order etc.). Recently, attempts have been made to study this information from a quantitative point of view: there is an oracle that delivers (per-instance) best-case information of a limited size, and the relationship between the amount of the additional information, and the benefit it can provide to the algorithm, is investigated. We show cases where this relationship has a form of a trade-off, and others where one or more thresholds can be identified.

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Authors and Affiliations

  1. Comenius University, Bratislava, Slovakia

    Rastislav Královič

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  1. Rastislav Královič
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Editors and Affiliations

  1. Dep. Computer Sci., P. J. Šafárik University, Jesenná 5, 04154, Košice, Slovakia

    Viliam Geffert

  2. Katholieke Universiteit Leuven, 3001, Leuven-Heverlee, Belgium

    Bart Preneel

  3. Department of Computer Science, Comenius University, Mlynska Dolina, 84248, Bratislava, Slovakia

    Branislav Rovan

  4. Institute of Computer Science, Academy of Sciences, 18207, Prague, Czech Republic

    Július Štuller

  5. Institute of Software Technology, Vienna University of Technology, Favoritenstraße 9-11 / 188, 1040, Vienna, Austria

    A Min Tjoa

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Královič, R. (2014). Advice Complexity: Quantitative Approach to A-Priori Information. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds) SOFSEM 2014: Theory and Practice of Computer Science. SOFSEM 2014. Lecture Notes in Computer Science, vol 8327. Springer, Cham. https://doi.org/10.1007/978-3-319-04298-5_3

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