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单词 Algorithmic entropy
释义

Algorithmic entropy

中文百科

柯氏复杂性 Kolmogorov complexity

(重定向自Algorithmic entropy)
柯氏复杂性 K(s),与两个下界可计算函数 prog1(s), prog2(s)。横坐标 (对数坐标) 列举了所有字符串 s,以长度排序;纵坐标(线性坐标)使用比特来标示字符串的长度。大部分的串是不可压缩的,也就是说它们的柯氏复杂度比它们的长度要多一个常数。图中展示了17个不可压缩的字符串,是几乎垂直的那些线段。根据柴廷不完全定理,任何计算柯氏复杂性的下界的进程都不可能超过某个极限,且不取决于输入的字符串 s。

在算法信息论(计算机科学和数学的一个分支)中,一个对象比如一段文本的柯氏复杂性(亦作柯尔莫哥洛夫复杂性描述复杂性柯尔莫哥洛夫-柴廷复杂度随机复杂度,或算法熵)是衡量描述这个对象所需要的信息量的一个尺度。柯氏复杂性是由安德雷·柯尔莫哥洛夫于1963年发现,所以用他的名字命名。

以下面的两个长度为64的字符串为例。

第一个字符串可以用中文简短地描述为“重复32个‘01’”。第二个字符串没有明显的简短描述。

一个字符串s的柯氏复杂性(C(s)或者K(s),区别如后)是这个字符串的最短描述的长度。换言之,一个字符串s的柯氏复杂性是能够输出且仅输出这个字符串的最短计算机/图灵机进程的长度。

英语百科

Kolmogorov complexity 柯氏复杂性

(重定向自Algorithmic entropy)
This image illustrates part of the Mandelbrot set fractal. Simply storing the 24-bit color of each pixel in this image would require 1.62 million bits, but a small computer program can reproduce these 1.62 million bits using the definition of the Mandelbrot set and the coordinates of the corners of the image. Thus, the Kolmogorov complexity of the raw file encoding this bitmap is much less than 1.62 million bits in any pragmatic model of computation.
Kolmogorov complexity K(s), and two computable lower bound functions prog1(s), prog2(s). The horizontal axis (logarithmic scale) enumerates all strings s, ordered by length; the vertical axis (linear scale) measures string length in bits. Most strings are incompressible, i.e. their Kolmogorov complexity exceeds their length by a constant amount. 17 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.

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 the 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 descriptive complexity, Kolmogorov–Chaitin complexity, algorithmic entropy, or program-size complexity. It is named after Andrey Kolmogorov, who first published on the subject in 1963.

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更新时间:2025/6/21 9:06:52