Binary Search Trees
... A linked structure is desirable if we do very much inserting and deleting. ...
... A linked structure is desirable if we do very much inserting and deleting. ...
Binary Trees
... if(prev == null) { root = new TreeNode(key, data); } else if(prev.key.compareTo(key) < 0) prev.right = new TreeNode(key, data); else prev.left = new TreeNode(key, data); ...
... if(prev == null) { root = new TreeNode(key, data); } else if(prev.key.compareTo(key) < 0) prev.right = new TreeNode(key, data); else prev.left = new TreeNode(key, data); ...
2-3-4 Trees - Randomly Philled
... also the tree’s maximum value) • Because of the way the insert and split algorithms work, it is rare to see full nodes that haven’t been split on each level • Also, even if each node on each level was full when visited, the number of data item searches will still be O(log n) proportional to the tota ...
... also the tree’s maximum value) • Because of the way the insert and split algorithms work, it is rare to see full nodes that haven’t been split on each level • Also, even if each node on each level was full when visited, the number of data item searches will still be O(log n) proportional to the tota ...
Big Idea - Department of Computer Science
... CM and total mass (TM) of all the particles it contains … “post order traversal” of QuadTree, cost = O(N log2N) or O(bN) 3) For each particle, traverse the QuadTree to compute the force on it, using the CM and TM of “distant” subsquares … core of algorithm … cost depends on accuracy desired but stil ...
... CM and total mass (TM) of all the particles it contains … “post order traversal” of QuadTree, cost = O(N log2N) or O(bN) 3) For each particle, traverse the QuadTree to compute the force on it, using the CM and TM of “distant” subsquares … core of algorithm … cost depends on accuracy desired but stil ...
Comparison of Brute-Force and KD Tree Algorithm
... the following way: every node in the tree is associated with one of the k dimensions, with the hyper-plane perpendicular to that dimension's axis. So, for example, if for a particular split the "x" axis is chosen, all points in the subtree with a smaller "x" value than the node will appear in the le ...
... the following way: every node in the tree is associated with one of the k dimensions, with the hyper-plane perpendicular to that dimension's axis. So, for example, if for a particular split the "x" axis is chosen, all points in the subtree with a smaller "x" value than the node will appear in the le ...
CPS 214: Networks and Distributed Systems Lecture 4
... n = my successor if my-id < n < key-id call Lookup(key-id) on node n // next hop else return my successor // done • Correctness depends only on successors • Q1: will this algorithm miss the real successor? • Q2: what’s the average # of lookup hops? ...
... n = my successor if my-id < n < key-id call Lookup(key-id) on node n // next hop else return my successor // done • Correctness depends only on successors • Q1: will this algorithm miss the real successor? • Q2: what’s the average # of lookup hops? ...
Quadtree
A quadtree is a tree data structure in which each internal node has exactly four children. Quadtrees are most often used to partition a two-dimensional space by recursively subdividing it into four quadrants or regions. The regions may be square or rectangular, or may have arbitrary shapes. This data structure was named a quadtree by Raphael Finkel and J.L. Bentley in 1974. A similar partitioning is also known as a Q-tree. All forms of quadtrees share some common features: They decompose space into adaptable cells Each cell (or bucket) has a maximum capacity. When maximum capacity is reached, the bucket splits The tree directory follows the spatial decomposition of the quadtree.