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  2. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    It iteratively does hill-climbing, each time with a random initial condition . The best is kept: if a new run of hill climbing produces a better than the stored state, it replaces the stored state. Random-restart hill climbing is a surprisingly effective algorithm in many cases.

  3. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    Conversely, a beam width of 1 corresponds to a hill-climbing algorithm. [3] The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists).

  4. Nelder–Mead method - Wikipedia

    en.wikipedia.org/wiki/Nelder–Mead_method

    Nelder–Mead (Downhill Simplex) explanation and visualization with the Rosenbrock banana function; John Burkardt: Nelder–Mead code in Matlab - note that a variation of the Nelder–Mead method is also implemented by the Matlab function fminsearch. Nelder-Mead optimization in Python in the SciPy library.

  5. Mean shift - Wikipedia

    en.wikipedia.org/wiki/Mean_shift

    Mean-shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region until convergence. Every shift is defined by a mean shift vector. The mean shift vector always points toward the direction of the maximum increase in the density.

  6. Stochastic hill climbing - Wikipedia

    en.wikipedia.org/wiki/Stochastic_hill_climbing

    Stochastic hill climbing is a variant of the basic hill climbing method. While basic hill climbing always chooses the steepest uphill move, "stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move."

  7. Hill-climbing algorithm - Wikipedia

    en.wikipedia.org/?title=Hill-climbing_algorithm&...

    move to sidebar hide. From Wikipedia, the free encyclopedia

  8. Local search (constraint satisfaction) - Wikipedia

    en.wikipedia.org/wiki/Local_search_(constraint...

    Hill climbing algorithms can only escape a plateau by doing changes that do not change the quality of the assignment. As a result, they can be stuck in a plateau where the quality of assignment has a local maxima. GSAT (greedy sat) was the first local search algorithm for satisfiability, and is a form of hill climbing.

  9. Iterative method - Wikipedia

    en.wikipedia.org/wiki/Iterative_method

    A specific implementation with termination criteria for a given iterative method like gradient descent, hill climbing, Newton's method, or quasi-Newton methods like BFGS, is an algorithm of an iterative method or a method of successive approximation.