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  2. HackerRank - Wikipedia

    en.wikipedia.org/wiki/HackerRank

    HackerRank's programming challenges can be solved in a variety of programming languages (including Java, C++, PHP, Python, SQL, and JavaScript) and span multiple computer science domains. [ 2 ] HackerRank categorizes most of their programming challenges into a number of core computer science domains, [ 3 ] including database management ...

  3. Competitive programming - Wikipedia

    en.wikipedia.org/wiki/Competitive_programming

    Irrespective of the problem category, the process of solving a problem can be divided into two broad steps: constructing an efficient algorithm, and implementing the algorithm in a suitable programming language (the set of programming languages allowed varies from contest to contest). These are the two most commonly tested skills in programming ...

  4. Range minimum query - Wikipedia

    en.wikipedia.org/wiki/Range_minimum_query

    Range minimum query reduced to the lowest common ancestor problem.. Given an array A[1 … n] of n objects taken from a totally ordered set, such as integers, the range minimum query RMQ A (l,r) =arg min A[k] (with 1 ≤ l ≤ k ≤ r ≤ n) returns the position of the minimal element in the specified sub-array A[l …

  5. Change-making problem - Wikipedia

    en.wikipedia.org/wiki/Change-making_problem

    The following is a dynamic programming implementation (with Python 3) which uses a matrix to keep track of the optimal solutions to sub-problems, and returns the minimum number of coins, or "Infinity" if there is no way to make change with the coins given. A second matrix may be used to obtain the set of coins for the optimal solution.

  6. Clique problem - Wikipedia

    en.wikipedia.org/wiki/Clique_problem

    The clique decision problem is not of practical importance; it is formulated in this way in order to apply the theory of NP-completeness to clique-finding problems. [19] The clique problem and the independent set problem are complementary: a clique in G is an independent set in the complement graph of G and vice versa. [20]

  7. Approximate string matching - Wikipedia

    en.wikipedia.org/wiki/Approximate_string_matching

    A fuzzy Mediawiki search for "angry emoticon" has as a suggested result "andré emotions" In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly).

  8. Breadth-first search - Wikipedia

    en.wikipedia.org/wiki/Breadth-first_search

    Breadth-first search can be used to solve many problems in graph theory, for example: Copying garbage collection, Cheney's algorithm; Finding the shortest path between two nodes u and v, with path length measured by number of edges (an advantage over depth-first search) [14] (Reverse) Cuthill–McKee mesh numbering

  9. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    Solution of a travelling salesman problem: the black line shows the shortest possible loop that connects every red dot. In the theory of computational complexity, the travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the ...