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The algorithm will determine, for any instance of the problem, whether a stable matching exists, and if so, will find such a matching. Irving's algorithm has O(n 2) complexity, provided suitable data structures are used to implement the necessary manipulation of the preference lists and identification of rotations.
The scenario approach with regularization has also been considered, [5] and handy algorithms with reduced computational complexity are available. [6] Extensions to more complex, non-convex, set-ups are still objects of active investigation. Along the scenario approach, it is also possible to pursue a risk-return trade-off.
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 ...
The method is useful for calculating the local minimum of a continuous but complex function, especially one without an underlying mathematical definition, because it is not necessary to take derivatives. The basic algorithm is simple; the complexity is in the linear searches along the search vectors, which can be achieved via Brent's method.
Bellman–Ford algorithm; Bianconi–Barabási model; Bidirectional search; Blossom algorithm; Borůvka's algorithm; Bottleneck traveling salesman problem; Brandes' algorithm; Breadth-first search; Bron–Kerbosch algorithm; Bully algorithm
Search-based software engineering is applicable to almost all phases of the software development process. Software testing has been one of the major applications. [9] Search techniques have been applied to other software engineering activities, for instance, requirements analysis, [10] [11] design, [12] [13] refactoring, [14] development, [15 ...
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation:
The algorithm has an asymptotically optimal cache complexity under the Ideal cache model. [11] Interestingly, the algorithm itself is cache-oblivious [11] meaning that it does not make any choices based on the cache parameters (e.g., cache size and cache line size) of the machine.