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  2. Rubber duck debugging - Wikipedia

    en.wikipedia.org/wiki/Rubber_duck_debugging

    The duck appeared at the bottom right corner of the browser viewport, and attempted to help visitors by listening to their problems and responding with solutions. However, the duck merely produced a quack sound after apparently thinking and typing. It referenced rubber ducking as a powerful method for solving problems. [9]

  3. Project Euler - Wikipedia

    en.wikipedia.org/wiki/Project_Euler

    Special awards exist for solving special combinations of problems. For instance, there is an award for solving fifty prime numbered problems. A special "Eulerians" level exists to track achievement based on the fastest fifty solvers of recent problems so that newer members can compete without solving older problems.

  4. CS50 - Wikipedia

    en.wikipedia.org/wiki/CS50

    CS50 (Computer Science 50) [a] is an introductory course on computer science taught at Harvard University by David J. Malan. The on-campus version of the course is Harvard's largest class with 800 students, 102 staff, and up to 2,200 participants in their regular hackathons .

  5. Competitive programming - Wikipedia

    en.wikipedia.org/wiki/Competitive_programming

    Competitive programming or sport programming is a mind sport involving participants trying to program according to provided specifications. The contests are usually held over the Internet or a local network. Competitive programming is recognized and supported by several multinational software and Internet companies, such as Google, [1] [2] and ...

  6. HiGHS optimization solver - Wikipedia

    en.wikipedia.org/wiki/HiGHS_optimization_solver

    Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, JavaScript, Fortran, and C#. It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core ...

  7. Constraint satisfaction problem - Wikipedia

    en.wikipedia.org/wiki/Constraint_satisfaction...

    Constraint satisfaction problems on finite domains are typically solved using a form of search. The most used techniques are variants of backtracking, constraint propagation, and local search. These techniques are also often combined, as in the VLNS method, and current research involves other technologies such as linear programming. [14]

  8. Subset sum problem - Wikipedia

    en.wikipedia.org/wiki/Subset_sum_problem

    The subset sum problem (SSP) is a decision problem in computer science.In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely . [1]

  9. Divide-and-conquer algorithm - Wikipedia

    en.wikipedia.org/wiki/Divide-and-conquer_algorithm

    A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.