In theoretical computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation. Markov algorithms are named after the Soviet mathematician Andrey Markov, Jr.
Refal is a programming language based on Markov algorithms.
Normal algorithms are verbal, that is, intended to be applied to strings in different alphabets.
The definition of any normal algorithm consists of two parts: the definition of the alphabet of the algorithm (the algorithm will be applied to strings of these alphabet symbols), and the definition of its scheme. The scheme of a normal algorithm is a finite ordered set of so-called substitution formulas, each of which can be simple or final. Simple substitution formulas are represented by strings of the form , where and are two arbitrary strings in the alphabet of the algorithm (called, respectively, the left and right sides of the formula substitution). Similarly, final substitution formulas are represented by strings of the form , where and are two arbitrary strings in the alphabet of the algorithm. This assumes that the auxiliary characters and do not belong to the alphabet of the algorithm (otherwise two other characters to perform their role as the dividers of the left and right sides, which are not in the algorithm's alphabet, should be selected).
Here is an example of a normal algorithm scheme in the five-letter alphabet :
The process of applying the normal algorithm to an arbitrary string in the alphabet of this algorithm is a discrete sequence of elementary steps, consisting of the following. Let’s assume that is the word obtained in the previous step of the algorithm (or the original word , if the current step is the first). If of the substitution formulas there is no left-hand side which is included in the , then the algorithm terminates, and the result of its work is considered to be the string .
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Computability is the ability to solve a problem in an effective manner. It is a key topic of the field of computability theory within mathematical logic and the theory of computation within computer science. The computability of a problem is closely linked to the existence of an algorithm to solve the problem. The most widely studied models of computability are the Turing-computable and μ-recursive functions, and the lambda calculus, all of which have computationally equivalent power.
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 computability theory, a system of data-manipulation rules (such as a model of computation, a computer's instruction set, a programming language, or a cellular automaton) is said to be Turing-complete or computationally universal if it can be used to simulate any Turing machine (devised by English mathematician and computer scientist Alan Turing). This means that this system is able to recognize or decide other data-manipulation rule sets. Turing completeness is used as a way to express the power of such a data-manipulation rule set.
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (O ...