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Welcome to the cs-notation wiki!
The goal of this wiki is to make computer science (CS) research papers more accessible by describing and explaining notation.
Minimal additions to the wiki consist of:
- The symbol in question (should be a specialized, CS-related usage, eg "1" is too common but × is not); include Unicode and ASCII versions if possible, LaTeX is optional.
- The meaning of the symbol (emphasize understanding rather than technical correctness)
- A citation/reference (not necessarily in an academic paper, but a recognized, credible source within CS/programming communities)
- A usage example (either mathematical or in an English sentence with sufficient context)
Examples of additional content could include:
- Implementation or usage examples in programming languages (eg
Math.ceil(x)in Python for ⌈x⌉)
- Usages in academic CS papers (including alternate uses)
Symbols from math (especially mathematical disciplines closely related to or widely used within CS such as linear algebra), statistics, engineering, linguistics, etc are all welcome.
% ("percent sign")
- Modulo operator: the remainder left after division, eg
65 % 7is the remainder left after dividing 65 by 7 (in this case
65 % 7 = 2because
65 = 7 * 9 + 2)
- Percentage: expresses a proportion as the numerator in x/100, eg 68% equals 0.68
Symbols are organized by category to encourage the discovery of related symbols. The current categories and their descriptions are:
- Linguistic symbols (eg Roman numerals, Greek alphabet)
- Mathematical operators (eg modulo, ceiling, floor)
- Logical symbols (eg quantifiers, negation, implication)
- Computational symbols (eg big-oh notation, lamba combinator)
Note that the same symbol might be encountered in multiple contexts. We expect people to see a symbol and come here to look it up. To aid with this, consider including verbal descriptors (eg "upside-down v" for
^ in addition to the symbol's technical name.)
This wiki was encouraged by Episode 34 of the Turing incomplete podcast.
Ideally, the wiki would have sufficient content to enable a non-CS major to read the papers listed in Papers We Love, Ten Technical Papers Every Programmer Should Read Twice, or 100 Years of Computer Science.
Additional resources include: