Decision Trees of Algorithms and a Semivaluation to Measure Their Distance
M. O' Keeffe, Homeira Pajoohesh, Michel Schellekens
Abstract
M. O' Keeffe, Homeira Pajoohesh, Michel Schellekens
Abstract
We use the set T n of binary trees with n leaves to study decision trees of algorithms. The set T n of binary trees with n leaves can be ordered by the so called “imbalance” order, where two trees are related in the order iff the second is less “balanced” than the first. This order forms a lattice. We show that this lattice is nonmodular and extend the imbalance lattice with an algebraic operation. The operation corresponds to the extension of a binary tree with new binary trees at the leafs, which reflects the effect of recursive calls in an algorithm on the decision tree and we will characterize as an illustration the decision tree of the insertion sort algorithm. We investigate the semivaluations on the binary trees which is related to the running time of the algorithm.
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We use the set T n of binary trees with n leaves to study decision trees of algorithms. The set T n of binary trees with n leaves can be ordered by the so called “imbalance” order, where two trees are related in the order iff the second is less “balanced” than the first. This order forms a lattice. We show that this lattice is nonmodular and extend the imbalance lattice with an algebraic operation. The operation corresponds to the extension of a binary tree with new binary trees at the leafs, which reflects the effect of recursive calls in an algorithm on the decision tree and we will characterize as an illustration the decision tree of the insertion sort algorithm. We investigate the semivaluations on the binary trees which is related to the running time of the algorithm.
Key concepts: Ternary search tree, Weight-balanced tree, Random binary tree, Binary search tree, Binary tree, Binary decision diagram, sort, Optimal binary search tree