'Binary search tree' presentation slideshows

Recall: Representation Changes:

Recall: Representation Changes: Gaussian elimination solves a system of linear equations by first transforming it to another system that makes finding a solution quite easy.

2.26k views • 213 slides

Chapter 20 Lists, Stacks, Queues, Trees, and Heaps

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Binary Search Trees

Binary Search Trees. Dictionary Operations: get(key) put(key, value) remove(key) Additional operations: ascend() get(index) (indexed binary search tree) remove(index) (indexed binary search tree). n is number of elements in dictionary.

591 views • 40 slides

Evaluating and Tuning a Static Analysis to Find Null Pointer Bugs

Evaluating and Tuning a Static Analysis to Find Null Pointer Bugs. Dave Hovemeyer Bill Pugh Jaime Spacco. How hard is it to find null-pointer exceptions?. Large body of work academic research too much to list on one slide commercial applications PREFix / PREFast Coverity Polyspace.

880 views • 39 slides

Evaluating and Tuning a Static Analysis to Find Null Pointer Bugs

Evaluating and Tuning a Static Analysis to Find Null Pointer Bugs. Dave Hovemeyer Bill Pugh Jaime Spacco. How hard is it to find null-pointer exceptions?. Large body of work academic research too much to list on one slide commercial applications PREFix / PREFast Coverity Polyspace.

512 views • 39 slides

Binary Trees

Binary Trees. Overview. Trees. Terminology. Traversal of Binary Trees. Expression Trees. Binary Search Trees. Trees. Family Trees. Organisation Structure Charts. Program Design. Structure of a chapter in a book. Parts of a Tree. Parts of a Tree. nodes. Parts of a Tree. parent node.

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CS 332: Algorithms

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CSE 326: Data Structures Trees

652 views • 34 slides

A Binary Search Tree Implementation

A Binary Search Tree Implementation. Chapter 17. Getting Started An Interface for the Binary Search Tree Duplicate Entries Beginning the Class Definition Searching and Retrieving Traversing Adding an Entry Recursive Implementation Removing an Entry Whose Node is a leaf.

645 views • 32 slides

Chapter 7

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B-Trees

B-Trees. Large degree B-trees used to represent very large dictionaries that reside on disk. Smaller degree B-trees used for internal-memory dictionaries to overcome cache-miss penalties. AVL Trees. n = 2 30 = 10 9 (approx). 30 <= height <= 43 .

459 views • 29 slides

B-Trees

B-Trees. Large degree B-trees used to represent very large dictionaries that reside on disk. Smaller degree B-trees used for internal-memory dictionaries to overcome cache-miss penalties. AVL Trees. n = 2 30 = 10 9 (approx). 30 <= height <= 43 .

487 views • 29 slides

Proving termination conditions

Proving termination conditions . Mentor : Dr. Ben Livshits & Dr. Stephan Tobies. The Project. The aim: Investigate state of the art approaches for termination proof; Prove termination of sample algorithms. We focused on the following cases: Nested loops ; Recursion ;

592 views • 25 slides

AVL-Trees

COMP171 Fall 2005. AVL-Trees. Balanced binary tree. The disadvantage of a binary search tree is that its height can be as large as N-1 This means that the time needed to perform insertion and deletion and many other operations can be O(N) in the worst case We want a tree with small height

1.3k views • 24 slides

AVL Search Trees

AVL Search Trees. What is an AVL Tree? AVL Tree Implementation. Why AVL Trees? Rotations. What is an AVL Tree?. An AVL tree is a binary search tree with a height balance property: For each node v, the heights of the subtrees of v differ by at most 1.

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Priority Queues

481 views • 14 slides

Mathematical Induction II

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Binary Search Trees

Binary Search Trees. 6. <. 2. 9. >. =. 8. 1. 4. Ordered Dictionaries. Keys are assumed to come from a total order. New operations: first (): first entry in the dictionary ordering last (): last entry in the dictionary ordering

299 views • 10 slides

Optimal binary search trees

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Set Implementations

Set Implementations. Bit Vector Linked List (Sorted and Unsorted) Hash Table Search Trees. Bit Vector. A = 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 1 U = a i , 0 <= i <= 15 A = {a 0 ,a 3 ,a 4 ,a 7 ,a 9 ,a 12 ,a 13 ,a 15 } 1 Bit per possible element

235 views • 7 slides