Making a fast unstable sorting algorithm stable1Arne Maus
adjective (computer science, of a sorting algorithm) That maintains the relative order of items that compare as equal. noun A building, wing or dependency set apart and adapted for lodging and feeding (and training) animals with hoofs, especially horses. noun (metonymically) All the rac...
unstable equilibrium , in which the body if disturbed does not tend to return to its former position, but to move farther away from it, as in the case of a body supported at a point below the center of gravity. Cf. Neutral equilibrium ...
Glidesort is a novel stable sorting algorithm that combines the best-case behavior of Timsort-style merge sorts for pre-sorted data with the best-case behavior of pattern-defeating quicksort for data with many duplicates. It is a comparison-based sort supporting arbitrary comparison operators, and...
The Sort methods used by the .NET collections are unstable.These sort methods, which includeSystem.Array.SortandSystem.Collections.Generic.List<T>.Sort, use the QuickSort algorithm, which is relatively fast but in this case, unstable. However, there may be instances where you require a stable...
Algorithm Partitioning is analogous to sorting an array of 0's and 1's, where elements smaller than the pivot are 0 and elements larger are 1. (Munro et al. 1990) Logsort sorts 0's and 1's stably in O(n) time and O(log n) space via its partition. ...
This unstable selectivity may be well-suited for representing variables that vary smoothly in time, such as spatial position, or for generating an eligibility trace for learning. In contrast, the population representations in OFC were substantially stable, i.e., the selectivity of a given neuron ...
Cohesive sediment forms flocs of various sizes and structures in the natural turbulent environment. Understanding flocculation is critical in accurately predicting sediment transport and biogeochemical cycles. In addition to aggregation and breakup, turb
This unstable selectivity may be well-suited for representing variables that vary smoothly in time, such as spatial position, or for generating an eligibility trace for learning. In contrast, the population representations in OFC were sub- stantially stable, i.e., the selectivity of a given ...
[20], simulated annealing and genetic algorithms can be regarded as classical randomised search methods [21–23]. Search procedures embedded into a given learning algorithm where features are ranked or weighted in the context of a classification task are called embedded methods. Popular embedded ...