PPT-CSE332: Data Abstractions

Lecture 9 B Trees Dan Grossman Spring 2010 Our goal Problem A dictionary with so much data most of it is on disk Desire A balanced tree logarithmic height that is even shallower than AVL trees so that we can minimize disk accesses and exploit diskblock size. Lecture 5: Binary Heaps, Continued. Tyler Robison. Summer 2010. 1. Review. 2. Priority Queue ADT: . insert. comparable object, . deleteMin. Binary heap data structure: Complete binary tree where each node has a lesser priority than its parent (greater value). Lecture 21: Amortized Analysis. Dan Grossman. Spring 2010. Amortized . Recall our plain-old stack implemented as an array that doubles its size if it runs out of room. How can we claim . push. is . O. Lecture . 27. : . A Few Words on NP. Dan Grossman. Spring 2010. This does not belong in CSE332. This lecture mentions some highlights of . NP. , the . P. vs. . NP. question, and . NP. -completeness. Lecture 7: AVL Trees. Tyler Robison. Summer 2010. 1. The AVL Tree Data Structure. An AVL tree is a BST. In addition: Balance . property:. balance of every node is. between -1 and . 1. balance. (. node. Dictionary ADT. : Arrays, Lists and . Trees. Kate Deibel. Summer 2012. June 27, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Where We Are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them. Disjoint Set Union-Find . and . Minimum Spanning Trees. Kate Deibel. Summer 2012. August 13, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Making Connections. You have a set of nodes (numbered 1-9) on a network. . Graphs and Graph Traversals. Kate Deibel. Summer 2012. July 25, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Last Time. We introduced the idea of graphs and their associated terminology. Key terms included:. CSE 332 Data Abstractions: A Heterozygous Forest of AVL, Splay, and B Trees Kate Deibel Summer 2012 July 2, 2012 CSE 332 Data Abstractions, Summer 2012 1 From last time… Binary search trees can give us great performance due to providing a structured binary search. Lecture 19: Analysis of Fork-Join Parallel Programs. Dan Grossman. Spring 2010. Where are we. Done:. How to use . fork. , and . join. to write a parallel algorithm. Why using divide-and-conquer with lots of small tasks is best. Lecture 6: Dictionaries; Binary Search Trees. Dan Grossman. Spring 2010. Where we are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them. ADTs so far:. Lecture 2: Math Review; Algorithm Analysis. Dan Grossman. Spring 2010. Announcements. Project 1 posted. Section materials on using Eclipse will be very useful if you have never used it. (Could also start in a different environment if necessary). Lecture 15: Introduction to Graphs. Dan Grossman. Spring 2010. Graphs. A graph is a formalism for representing relationships among items. Very general definition because very general concept. A . graph. Lecture 5: Binary Heaps, Continued. Dan Grossman. Spring 2010. Review. Priority Queue ADT: . insert. comparable object, . deleteMin. Binary heap data structure: Complete binary tree where each node has priority value greater than its parent. Dictionary ADT. : Arrays, Lists and . Trees. Kate Deibel. Summer 2012. June 27, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Where We Are. Studying the absolutely essential ADTs of computer science and classic data structures for implementing them.