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A common technique for proving the correctness of greedy algorithms uses an inductive exchange argument. [3] The exchange argument demonstrates that any solution different from the greedy solution can be transformed into the greedy solution without degrading its quality. This proof pattern typically follows these steps:
Charging arguments can also be used to show approximation results. In particular, it can be used to show that an algorithm is an n-approximation to an optimization problem. Instead of showing that an algorithm produces outputs with the same value of profit or cost as the optimal solution, show that it attains that value within a factor of n.
The farthest-first traversal of a finite point set may be computed by a greedy algorithm that maintains the distance of each point from the previously selected points, performing the following steps: [3] Initialize the sequence of selected points to the empty sequence, and the distances of each point to the selected points to infinity.
A greedy algorithm is optimal for every R-compatible linear objective function over a greedoid. The intuition behind this proposition is that, during the iterative process, each optimal exchange of minimum weight is made possible by the exchange property, and optimal results are obtainable from the feasible sets in the underlying greedoid.
This proves that the greedy algorithm indeed finds an optimal solution. A more formal explanation is given by a Charging argument. The greedy algorithm can be executed in time O(n log n), where n is the number of tasks, using a preprocessing step in which the tasks are sorted by their finishing times.
These algorithms typically do not work well for larger read sets, as they do not easily reach a global optimum in the assembly, and do not perform well on read sets that contain repeat regions. [1] Early de novo sequence assemblers, such as SEQAID [2] (1984) and CAP [3] (1992), used greedy algorithms, such as overlap-layout-consensus (OLC ...
Another example is attempting to make 40 US cents without nickels (denomination 25, 10, 1) with similar result — the greedy chooses seven coins (25, 10, and 5 × 1), but the optimal is four (4 × 10). A coin system is called "canonical" if the greedy algorithm always solves its change-making problem optimally.
The nearest neighbour algorithm is easy to implement and executes quickly, but it can sometimes miss shorter routes which are easily noticed with human insight, due to its "greedy" nature. As a general guide, if the last few stages of the tour are comparable in length to the first stages, then the tour is reasonable; if they are much greater ...