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  2. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Adaptive simulated annealing algorithms address this problem by connecting the cooling schedule to the search progress. Other adaptive approaches such as Thermodynamic Simulated Annealing, [16] automatically adjusts the temperature at each step based on the energy difference between the two states, according to the laws of thermodynamics.

  3. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    The properties of gradient descent depend on the properties of the objective function and the variant of gradient descent used (for example, if a line search step is used). The assumptions made affect the convergence rate, and other properties, that can be proven for gradient descent. [ 33 ]

  4. Newton's method in optimization - Wikipedia

    en.wikipedia.org/wiki/Newton's_method_in...

    The geometric interpretation of Newton's method is that at each iteration, it amounts to the fitting of a parabola to the graph of () at the trial value , having the same slope and curvature as the graph at that point, and then proceeding to the maximum or minimum of that parabola (in higher dimensions, this may also be a saddle point), see below.

  5. Local search (optimization) - Wikipedia

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

    While it is sometimes possible to substitute gradient descent for a local search algorithm, gradient descent is not in the same family: although it is an iterative method for local optimization, it relies on an objective function’s gradient rather than an explicit exploration of the solution space.

  6. Adaptive simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Adaptive_simulated_annealing

    Adaptive simulated annealing (ASA) is a variant of simulated annealing (SA) algorithm in which the algorithm parameters that control temperature schedule and random step selection are automatically adjusted according to algorithm progress. This makes the algorithm more efficient and less sensitive to user defined parameters than canonical SA.

  7. Gradient method - Wikipedia

    en.wikipedia.org/wiki/Gradient_method

    In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)} with the search directions defined by the gradient of the function at the current point.

  8. Computer-generated holography - Wikipedia

    en.wikipedia.org/wiki/Computer-generated_holography

    Computer-generated holography (CGH) is a technique that uses computer algorithms to generate holograms.It involves generating holographic interference patterns.A computer-generated hologram can be displayed on a dynamic holographic display, or it can be printed onto a mask or film using lithography. [1]

  9. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    By contrast, gradient descent methods can move in any direction that the ridge or alley may ascend or descend. Hence, gradient descent or the conjugate gradient method is generally preferred over hill climbing when the target function is differentiable. Hill climbers, however, have the advantage of not requiring the target function to be ...