Partial Solution and Entropy

If the given problem instance is partially solved. we want to minimize our effort to solve the problem rising that information. Ill this paper we introduce the measure of entropy H(S) for uncertainty in partially solved input data S(X) = (X(1),...,X(k)), where X is the entire data set, and each X(i) is already solved. We use the entropy measure to analyze three example problems, sorting, shortest paths and minimum spanning trees. For sorting X(i) is an ascending run, and for shortest paths, X(i) is an acyclic part in the given graph. For minimum spanning trees, X(i) is interpreted as a partially obtained minimum spanning tree for a, subgraph. The entropy measure, H(S), is defined by regarding p(i) = vertical bar X(i)vertical bar/vertical bar X vertical bar as a probability measure, that is, H(S) = -n Sigma(k)(i=1) p(i) log p(i), where n = Sigma(k)(i=1) vertical bar X(i)vertical bar. Then we show that we can sort the input data S(X) in O(H(S)) time; and solve the shortest path problem in O(m + H(S)) time where in is the number of edges of the graph. Finally we show that the minimum spanning tree is computed in O(m + H(S)) time.