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  2. SIMPLE algorithm - Wikipedia

    en.wikipedia.org/wiki/SIMPLE_algorithm

    In computational fluid dynamics (CFD), the SIMPLE algorithm is a widely used numerical procedure to solve the Navier–Stokes equations. SIMPLE is an acronym for Semi-Implicit Method for Pressure Linked Equations. The SIMPLE algorithm was developed by Prof. Brian Spalding and his student Suhas Patankar at Imperial College London in the early ...

  3. Algorithm - Wikipedia

    en.wikipedia.org/wiki/Algorithm

    While many algorithms reach an exact solution, approximation algorithms seek an approximation that is close to the true solution. Such algorithms have practical value for many hard problems. For example, the Knapsack problem, where there is a set of items, and the goal is to pack the knapsack to get the maximum total value. Each item has some ...

  4. List of algorithms - Wikipedia

    en.wikipedia.org/wiki/List_of_algorithms

    An algorithm is fundamentally a set of rules or defined procedures that is typically designed and used to solve a specific problem or a broad set of problems.. Broadly, algorithms define process(es), sets of rules, or methodologies that are to be followed in calculations, data processing, data mining, pattern recognition, automated reasoning or other problem-solving operations.

  5. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    algorithm FPTAS is input: ε ∈ (0,1] a list A of n items, specified by their values, , and weights output: S' the FPTAS solution P := max {} // the highest item value K := ε for i from 1 to n do ′ := ⌊ ⌋ end for return the solution, S', using the ′ values in the dynamic program outlined above

  6. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    The currently best quantum exact algorithm for TSP due to Ambainis et al. runs in time (). [26] Other approaches include: Various branch-and-bound algorithms, which can be used to process TSPs containing thousands of cities. Solution of a TSP with 7 cities using a simple Branch and bound algorithm.

  7. P versus NP problem - Wikipedia

    en.wikipedia.org/wiki/P_versus_NP_problem

    An example is the simplex algorithm in linear programming, which works surprisingly well in practice; despite having exponential worst-case time complexity, it runs on par with the best known polynomial-time algorithms. [27]

  8. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    In graph theory and theoretical computer science, the longest path problem is the problem of finding a simple path of maximum length in a given graph.A path is called simple if it does not have any repeated vertices; the length of a path may either be measured by its number of edges, or (in weighted graphs) by the sum of the weights of its edges.

  9. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    The solution of a linear program is accomplished in two steps. In the first step, known as Phase I, a starting extreme point is found. Depending on the nature of the program this may be trivial, but in general it can be solved by applying the simplex algorithm to a modified version of the original program.