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Concept

Theory of computation

Résumé

In theoretical computer science and mathematics, the theory of computation is the branch that deals with what problems can be solved on a model of computation, using an algorithm, how efficiently they can be solved or to what degree (e.g., approximate solutions versus precise ones). The field is divided into three major branches: automata theory and formal languages, computability theory, and computational complexity theory, which are linked by the question: "What are the fundamental capabilities and limitations of computers?". In order to perform a rigorous study of computation, computer scientists work with a mathematical abstraction of computers called a model of computation. There are several models in use, but the most commonly examined is the Turing machine. Computer scientists study the Turing machine because it is simple to formulate, can be analyzed and used to prove results, and because it represents what many consider the most powerful possible "reasonable" model of comp

Source officielle

https://en.wikipedia.org/wiki/Theory_of_computation

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Publications associées (46)

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Personnes associées (4)

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Unités associées (4)

Unités associées

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Concepts associés (48)

Concepts associés

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Cours associés (39)

Cours associés

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Séances de cours associées (36)

Séances de cours associées

Chargement

On Probabilistic Fixpoint and Markov Chain Query Languages.

Christoph Koch

We study highly expressive query languages such as datalog, fixpoint, and while-languages on probabilistic databases. We generalize these languages such that computation steps (e.g. datalog rules) can fire probabilistically. We define two possible semantics for such query languages, namely inflationary semantics where the results of each computation step are added to the current database and non-inflationary queries that induce a random walk in-between database instances. We then study the complexity of exact and approximate query evaluation under these semantics.

2010

Chargement

Parallelizing an algorithm consists of dividing the computation into a set of sequential operations, assigning the operations to threads, synchronizing the execution of threads, specifying the data transfer requirements between threads and mapping the threads onto processors. With current software technology, writing a parallel program executing the parallelized algorithm involves mixing sequential code with calls to a communication library such as PVM, both for communication and synchronization. The authors introduce CAP (Computer-Aided Parallelization), a language extension to C++ from which C++/PVM programs are automatically generated. CAP allows to specify (1) the threads in a parallel program, (2) the messages exchanged between threads, and (3) the ordering of sequential operations required to complete a parallel task. All CAP operations (sequential and parallel) have a single input and a single output, and no shared variables. CAP separates completely the computation description from the communication and synchronization specification. From the CAP specification, a MPMD (multiple program multiple data) program is generated that executes on the various processing elements of the parallel machine. They illustrate the features of the CAP parallel programming extension to C++. They demonstrate the expressive power of CAP and the performance of CAP-specified applications

Springer-Verlag1996

Chargement

Function Computation over Networks

Chien-Yi Wang

This thesis looks at efficient information processing for two network applications: content delivery with caching and collecting summary statistics in wireless sensor networks. Both applications are studied under the same paradigm: function computation over networks, where distributed source nodes cooperatively communicate some functions of individual observations to one or multiple destinations. One approach that always works is to convey all observations and then let the destinations compute the desired functions by themselves. However, if the available communication resources are limited, then revealing less unwanted information becomes critical. Centered on this goal, this thesis develops new coding schemes using information-theoretic tools. The first part of this thesis focuses on content delivery with caching. Caching is a technique that facilitates reallocation of communication resources in order to avoid network congestion during peak-traffic times. An information-theoretic model, termed sequential coding for computing, is proposed to analyze the potential gains offered by the caching technique. For the single-user case, the proposed framework succeeds in verifying the optimality of some simple caching strategies and in providing guidance towards optimal caching strategies. For the two-user case, five representative subproblems are considered, which draw connections with classic source coding problems including the Gray-Wyner system, successive refinement, and the Kaspi/Heegard-Berger problem. Afterwards, the problem of distributed computing with successive refinement is considered. It is shown that if full data recovery is required in the second stage of successive refinement, then any information acquired in the first stage will be useful later in the second stage. The second part of this thesis looks at the collection of summary statistics in wireless sensor networks. Summary statistics include arithmetic mean, median, standard deviation, etc, and they belong to the class of symmetric functions. This thesis develops arithmetic computation coding in order to efficiently perform in-network computation for weighted arithmetic sums and symmetric functions. The developed arithmetic computation coding increases the achievable computation rate from

\Theta((\log L)/L)

\Theta(1/\log L)

, where

L

is the number of sensors. Finally, this thesis demonstrates that interaction among sensors is beneficial for computation of type-threshold functions, e.g., the maximum and the indicator function, and that a non-vanishing computation rate is achievable.

EPFL2015

Chargement

Function Computation over Networks

Chien-Yi Wang

\Theta((\log L)/L)

\Theta(1/\log L)

, where

L

EPFL2015