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Pattern search (also known as direct search, derivative-free search, or black-box search) is a family of numerical optimization methods that does not require a gradient. As a result, it can be used on functions that are not continuous or differentiable. One such pattern search method is "convergence" (see below), which is based on the theory of ...
It is a direct search method (based on function comparison) and is often applied to nonlinear optimization problems for which derivatives may not be known. However, the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points [1] on problems that can be solved by alternative methods. [2]
Another example of heuristic making an algorithm faster occurs in certain search problems. Initially, the heuristic tries every possibility at each step, like the full-space search algorithm. But it can stop the search at any time if the current possibility is already worse than the best solution already found.
Variable neighborhood search (VNS), [1] proposed by Mladenović & Hansen in 1997, [2] is a metaheuristic method for solving a set of combinatorial optimization and global optimization problems. It explores distant neighborhoods of the current incumbent solution, and moves from there to a new one if and only if an improvement was made.
If h a (n) is an admissible heuristic function, in the weighted version of the A* search one uses h w (n) = ε h a (n), ε > 1 as the heuristic function, and perform the A* search as usual (which eventually happens faster than using h a since fewer nodes are expanded).
In computer science, local search is a heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution that maximizes a criterion among a number of candidate solutions .
Because a constraint satisfaction problem can be interpreted as a local search problem when all the variables have an assigned value (called a complete state), the min conflicts algorithm can be seen as a repair heuristic [2] that chooses the state with the minimum number of conflicts.
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...