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  2. Hungarian algorithm - Wikipedia

    en.wikipedia.org/wiki/Hungarian_algorithm

    The Hungarian method is a combinatorial optimization algorithm that solves the assignment problem in polynomial time and which anticipated later primal–dual methods.It was developed and published in 1955 by Harold Kuhn, who gave it the name "Hungarian method" because the algorithm was largely based on the earlier works of two Hungarian mathematicians, Dénes Kőnig and Jenő Egerváry.

  3. Shape context - Wikipedia

    en.wikipedia.org/wiki/Shape_context

    Each object has 72 views in the database. In the experiment, the method was trained on a number of equally spaced views for each object and the remaining views were used for testing. A 1-NN classifier was used. The authors also developed an editing algorithm based on shape context similarity and k-medoid clustering that improved on their ...

  4. Pattern search (optimization) - Wikipedia

    en.wikipedia.org/wiki/Pattern_search_(optimization)

    Convergence is a pattern search method proposed by Yu, who proved that it converges using the theory of positive bases. [3] Later, Torczon, Lagarias and co-authors [4] [5] used positive-basis techniques to prove the convergence of another pattern-search method on specific classes of functions.

  5. Conjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Conjugate_gradient_method

    The above algorithm gives the most straightforward explanation of the conjugate gradient method. Seemingly, the algorithm as stated requires storage of all previous searching directions and residue vectors, as well as many matrix–vector multiplications, and thus can be computationally expensive.

  6. Biconjugate gradient method - Wikipedia

    en.wikipedia.org/wiki/Biconjugate_gradient_method

    In mathematics, more specifically in numerical linear algebra, the biconjugate gradient method is an algorithm to solve systems of linear equations A x = b . {\displaystyle Ax=b.\,} Unlike the conjugate gradient method , this algorithm does not require the matrix A {\displaystyle A} to be self-adjoint , but instead one needs to perform ...

  7. Powell's method - Wikipedia

    en.wikipedia.org/wiki/Powell's_method

    Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function. The function need not be differentiable, and no derivatives are taken. The function must be a real-valued function of a fixed number of real-valued inputs.

  8. Pointer jumping - Wikipedia

    en.wikipedia.org/wiki/Pointer_jumping

    Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by "jumping" to the end of the ...

  9. Any-angle path planning - Wikipedia

    en.wikipedia.org/wiki/Any-angle_path_planning

    Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. [ 1 ]