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  2. String trimmer - Wikipedia

    en.wikipedia.org/wiki/String_trimmer

    A fixed line style string trimmer head. A fixed-line string trimmer has a trimmer head that takes pre-cut line inserts. There is no wound spool of line in the trimmer head; the user instead feeds the pre-cut line in to the appropriate slots, making the trimmer easier to feed line into and troubleshoot than a bump feed system. [citation needed]

  3. C string handling - Wikipedia

    en.wikipedia.org/wiki/C_string_handling

    The length of a string is the number of code units before the zero code unit. [1] The memory occupied by a string is always one more code unit than the length, as space is needed to store the zero terminator. Generally, the term string means a string where the code unit is of type char, which is exactly 8 bits on all modern machines.

  4. Ryobi - Wikipedia

    en.wikipedia.org/wiki/Ryobi

    Ryobi Limited (English: / r aɪ ˈ oʊ b i / ry-OH-bee or / r i ˈ oʊ b i / ree-OH-bee; Japanese: リョービ株式会社, romanized: Ryōbi Kabushiki-gaisha, IPA: [ɾʲoːꜜbi]) is a Japanese manufacturer of components for automobiles, electronics, and telecommunications industries.

  5. Trimming (computer programming) - Wikipedia

    en.wikipedia.org/.../Trimming_(computer_programming)

    The most popular variants of the trim function strip only the beginning or end of the string. Typically named ltrim and rtrim respectively, or in the case of Python: lstrip and rstrip. C# uses TrimStart and TrimEnd, and Common Lisp string-left-trim and string-right-trim.

  6. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    This algorithm, an example of bottom-up dynamic programming, is discussed, with variants, in the 1974 article The String-to-string correction problem by Robert A. Wagner and Michael J. Fischer. [ 4 ] This is a straightforward pseudocode implementation for a function LevenshteinDistance that takes two strings, s of length m , and t of length n ...

  7. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    Maximize c T x subject to Ax ≤ b, x ≥ 0; with the corresponding symmetric dual problem, Minimize b T y subject to A T y ≥ c, y ≥ 0. An alternative primal formulation is: Maximize c T x subject to Ax ≤ b; with the corresponding asymmetric dual problem, Minimize b T y subject to A T y = c, y ≥ 0. There are two ideas fundamental to ...