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The cost of a list labeling algorithm is the number of label (re-)assignments per insertion or deletion. List labeling algorithms have applications in many areas, including the order-maintenance problem, cache-oblivious data structures, [1] data structure persistence, [2] graph algorithms [3] [4] and fault-tolerant data structures. [5]
Sorting a set of unlabelled weights by weight using only a balance scale requires a comparison sort algorithm. A comparison sort is a type of sorting algorithm that only reads the list elements through a single abstract comparison operation (often a "less than or equal to" operator or a three-way comparison) that determines which of two elements should occur first in the final sorted list.
The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory. Both areas are highly related, as the complexity of an algorithm is always an upper bound on the complexity of the problem solved by this algorithm. Moreover, for ...
The algorithm can be made much more effective by first sorting the list of items into decreasing order (sometimes known as the first-fit decreasing algorithm), although this still does not guarantee an optimal solution and for longer lists may increase the running time of the algorithm. It is known, however, that there always exists at least ...
The following is the skeleton of a generic branch and bound algorithm for minimizing an arbitrary objective function f. [3] To obtain an actual algorithm from this, one requires a bounding function bound, that computes lower bounds of f on nodes of the search tree, as well as a problem-specific branching rule.
An equivalent description of the FFD algorithm is as follows. Order the items from largest to smallest. While there are remaining items: Open a new empty bin. For each item from largest to smallest: If it can fit into the current bin, insert it. In the standard description, we loop over the items once, but keep many open bins.
Analyzing a particular algorithm falls under the field of analysis of algorithms. To show an upper bound () on the time complexity of a problem, one needs to show only that there is a particular algorithm with running time at most (). However, proving lower bounds is much more difficult, since lower bounds make a statement about all possible ...
The optimal algorithm is by Andris Ambainis. [7] Yaoyun Shi first proved a tight lower bound when the size of the range is sufficiently large. [8] Ambainis [9] and Kutin [10] independently (and via different proofs) extended his work to obtain the lower bound for all functions.