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Python: python.org: Python Software Foundation License: Python has two major implementations, the built in re and the regex library. Ruby: ruby-doc.org: GNU Library General Public License: Ruby 1.8, Ruby 1.9, and Ruby 2.0 and later versions use different engines; Ruby 1.9 integrates Oniguruma, Ruby 2.0 and later integrate Onigmo, a fork from ...
Besides the built-in RE/flex POSIX regex pattern matcher, RE/flex also supports PCRE2, Boost.Regex and std::regex pattern matching libraries. PCRE2 and Boost.Regex offer a richer regular expression pattern syntax with Perl pattern matching semantics, but are slower due to their intrinsic NFA-based matching algorithm.
In Python and some other implementations (e.g. Java), the three common quantifiers (*, + and ?) are greedy by default because they match as many characters as possible. [39] The regex ".+" (including the double-quotes) applied to the string "Ganymede," he continued, "is the largest moon in the Solar System."
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
Common applications of approximate matching include spell checking. [5] With the availability of large amounts of DNA data, matching of nucleotide sequences has become an important application. [1] Approximate matching is also used in spam filtering. [5] Record linkage is a common application where records from two disparate databases are matched.
The matching pursuit is an example of a greedy algorithm applied on signal approximation. A greedy algorithm finds the optimal solution to Malfatti's problem of finding three disjoint circles within a given triangle that maximize the total area of the circles; it is conjectured that the same greedy algorithm is optimal for any number of circles.
In computer programming and computer science, "maximal munch" or "longest match" is the principle that when creating some construct, as much of the available input as possible should be consumed. The earliest known use of this term is by R.G.G. Cattell in his PhD thesis [ 1 ] on automatic derivation of code generators for compilers .
In mathematics, economics, and computer science, the stable marriage problem (also stable matching problem) is the problem of finding a stable matching between two equally sized sets of elements given an ordering of preferences for each element. A matching is a bijection from the elements