Showing papers on "Interval tree published in 1976"

Combinatorial Merging

Abstract: Three algorithms are given to construct a weighted r-ary tree with a given set of integer weighted leaves in which the weight of a parent node is 1 + max of its sons. Two goals are of interest; to minimize 1) the weight of the root of the tree, and 2) the number of internal nodes.

b-Matchings in Trees

Abstract: We develop linear-time algorithms to find maximum weighted and unweighted degree-constrained subgraphs (b-matchings) of a tree. We use a generalization of an algorithm for finding a maximum 2-matching in a tree.

Self-organizing binary search trees

Abstract: We consider heuristics which attempt to maintain a binary search tree in a near optimal form, assuming that elements are requested with fixed, but unknown, independent probabilities. A "move to root" heuristic is shown to yield an expected search time within a constant factor of that of an optimal static binary search tree. On the other hand, a closely related "simple exchange" technique is shown not to have this property. The rate of convergence of the "move to root" heuristic is discussed. We also consider the more general case in which elements not in the tree may have non-zero probability of being requested.

Traversing Binary Tree Structures with Shift Register Memories.

Abstract: The paper proposes a tree-structured shift-register memory which employs two different data permutation operations to perform pre-/post-order traversals of binary tree structures.

The family binary tree

Abstract: Family pedigree data as required by genetics has proven to be difficult to analyze. This paper presents a data collection and structuring technique developed for the program PDGRE, which significantly improves on past computer application performances and produces a binary tree representation for the data. Data is collected in family units, mother, father, and offspring. The units are related by linked lists of those individuals who appear in more than one unit. The algorithm TRANS translates the links from relative pointers to actual memory locations. TRANS also locates those individuals who will be root nodes for the binary tree representations of the pedigree data. Algorithm BUILD creates the binary tree for any root passed to it. Each binary tree represents a subset of the original data set. The subsets prove more suited to analysis than the entire pedigree. The number of subsets is determined by the number of roots located by TRANS. The end result is data represented by a well known data structure at a significantly lower cost than previous applications.