stochastic complexity - translation to ρωσικά
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stochastic complexity - translation to ρωσικά

MEASURE OF ALGORITHMIC COMPLEXITY
Algorithmic complexity theory; Kolmogorov Complexity; Kolmogorov randomness; Chaitin-Kolmogorov randomness; K-complexity; Kolmogorov-Chaitin complexity; Kolmogorov-Chaitin randomness; Stochastic complexity; Algorithmic entropy; Program-size complexity; Chaitin–Kolmogorov randomness; Conditional complexity; Compressibility (computer science); Kolmogorov/Chaitin complexity; Chaitin's incompleteness theorem; Chaitin Complexity; Kolmogorov–Chaitin complexity; Kolmogorov–Chaitin randomness; Kolgomorov complexity; Conditional Kolmogorov complexity
  • strings]] ''s'', ordered by length; the vertical axis ([[linear scale]]) measures Kolmogorov complexity in [[bit]]s. Most strings are incompressible, i.e. their Kolmogorov complexity exceeds their length by a constant amount. 9 compressible strings are shown in the picture, appearing as almost vertical slopes. Due to Chaitin's incompleteness theorem (1974), the output of any program computing a lower bound of the Kolmogorov complexity cannot exceed some fixed limit, which is independent of the input string ''s''.

stochastic complexity         
стохастическая сложность
Kolmogorov complexity         
сложность по Колмогорову, колмогоровская сложность

Ορισμός

complexity
<algorithm> The level in difficulty in solving mathematically posed problems as measured by the time, number of steps or arithmetic operations, or memory space required (called time complexity, computational complexity, and space complexity, respectively). The interesting aspect is usually how complexity scales with the size of the input (the "scalability"), where the size of the input is described by some number N. Thus an algorithm may have computational complexity O(N^2) (of the order of the square of the size of the input), in which case if the input doubles in size, the computation will take four times as many steps. The ideal is a constant time algorithm (O(1)) or failing that, O(N). See also NP-complete. (1994-10-20)

Βικιπαίδεια

Kolmogorov complexity

In algorithmic information theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as algorithmic complexity, Solomonoff–Kolmogorov–Chaitin complexity, program-size complexity, descriptive complexity, or algorithmic entropy. It is named after Andrey Kolmogorov, who first published on the subject in 1963 and is a generalization of classical information theory.

The notion of Kolmogorov complexity can be used to state and prove impossibility results akin to Cantor's diagonal argument, Gödel's incompleteness theorem, and Turing's halting problem. In particular, no program P computing a lower bound for each text's Kolmogorov complexity can return a value essentially larger than P's own length (see section § Chaitin's incompleteness theorem); hence no single program can compute the exact Kolmogorov complexity for infinitely many texts.

Μετάφραση του &#39stochastic complexity&#39 σε Ρωσικά