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Splay Trees
Splay Trees

...  Replace t with a new tree consisting of a new root node containing x, whose left and right subtrees are t1 and t2 returned by the split. ...
Representation Tree Structures by Dynamic Lists
Representation Tree Structures by Dynamic Lists

... Keywords: Data structures, tree structure, Dynamic lists. I. INTRODUCTION Had long been working on the development of structures for store and retrieve data, derived from these studies has been created structures of various kinds, for example simply link lists, doubly linked lists, circular lists, t ...
Algorithms and Data Structures
Algorithms and Data Structures

pptx - Department of Computer Science
pptx - Department of Computer Science

... Persistence Techniques [N. Sarnak, R.E. Tarjan, Planar point location using persistent search trees, Communications of the ACM, 29(7), 669-679, 1986] ...
Transform-and-conquer
Transform-and-conquer

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Distributed Algorithms

Complete Binary Trees
Complete Binary Trees

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Data Structures and Algorithms IT2003
Data Structures and Algorithms IT2003

... ◦ General tree — each node can have an arbitrary number of children. ...
Chapter 20
Chapter 20

trees - Omieno Kelvin
trees - Omieno Kelvin

... to find a particular node. How long does it take to do this? In a full tree, about half the nodes are on the bottom level. (Actually there‘s one more node on the bottom row than in the rest of the tree.) Thus about half of all searches or insertions or deletions require finding a node on the lowest ...
CSE 326: Data Structures Lecture #7 Branching Out
CSE 326: Data Structures Lecture #7 Branching Out

Advanced Data Structure
Advanced Data Structure

... For each node, do shift down O(NlgN)??? For a binary tree with N nodes • There are at most ceil(N/2h+1) nodes with height h ...
Range and multidimensional searches
Range and multidimensional searches

... –  Keep(track(of(which(dimension(you(are(searching.( –  As(you(keep(searching(down,(be(sure(to(keep(track(of(which( dimension(the(nodes(are(being(split(on.( ...
Week 4 - Ken Cosh
Week 4 - Ken Cosh

Week 4 - Ken Cosh
Week 4 - Ken Cosh

...  An alternative to using the stack to store tree movements, is to transform the tree to make it easier to traverse.  Obviously a tree with no left nodes is easy to traverse by stepping to the right.  Morris’s algorithm makes use of this by changing LVR to VR (no need to step left). ...
Is it a Tree?
Is it a Tree?

... It is possible to sort an array using a binary search tree  Insert the array items into an empty tree  Write the data from the tree back into the array using an ...
Removal from a Binary Search Tree
Removal from a Binary Search Tree

... Problem: If we assume that the keys have been inserted into a binary search tree in random order, then, on average, how many more comparisons are needed in a search of the resulting tree than would be needed in a completely balanced tree? ...
pptx - Department of Computer Science
pptx - Department of Computer Science

... Persistence Techniques [N. Sarnak, R.E. Tarjan, Planar point location using persistent search trees, Communications of the ACM, 29(7), 669-679, 1986] ...
Advanced Data Structures - Department of Computer Science
Advanced Data Structures - Department of Computer Science

... Persistence Techniques [N. Sarnak, R.E. Tarjan, Planar point location using persistent search trees, Communications of the ACM, 29(7), 669-679, 1986] ...
Encoding Nearest Larger Values
Encoding Nearest Larger Values

... Unfortunately, none of the above sequences appears in the Online Encyclopedia of Integer Sequences4 . Consider the sequence generated by some arbitrary tie breaking rule. If zi is the i-th term in this sequence, then limn→∞ lg(zn )/n is the constant factor in the asymptotic space bound required to ...
Notes - CS.Duke
Notes - CS.Duke

Data Structure Important Questions Section A Section B
Data Structure Important Questions Section A Section B

... 1. Explain the difference between AVL and B tree. 2. Explain the various collision resolution techniques. 3. Describe the AVL trees insertion and deletion method. 4. How an AVL tree differs from a BST. 5. What are the different methods of opening a file? Explain in brief. 6. Write a program which co ...
Binary Search Tree
Binary Search Tree

...  This non-recursive (iterative) version of tree-search is usually ...
DATA STRUCTURES - UMD Department of Computer Science
DATA STRUCTURES - UMD Department of Computer Science

... data that is aggregated need not be restricted to the types that we have seen. It can also consist of instances of the same record type or other record types. In this case our fields contain information in the form of addresses of (called pointers or links to) other records. For example, returning t ...
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE

... 4. Traverse: This function allow user to visit each node in the list. (The purpose of the visit is defined by the list user and it is certain to vary from application to application) 5. isEmpty : This function returns a true/false indication of whether or not there are any nodes in the list. 6. isFu ...
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Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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