result [FYI]?. This involved studying weak MVDs, which were first defined in [JS] and generalized in [Tho]. We then obtained the "top down" version of this result,.
Questions about binary trees refer to the definitions on the last page for
BinaryTree, ... data structure is needed for an iterative (non-recursive) function
that does ...
CS 124. Algorithms and Data Structures. Overview. Michael Mitzenmacher. Page 2. Course Goal. ⢠Provide a solid backgro
Rochester, NY 14627-0226. {scherer,scott}@cs.rochester.edu. Abstract. We apply the classic theory ...... IBM Corporation, 1983. [6] L. Lamport. A New Solution of ...
People from all concentrations. (mathematics, biology, physics, economicsâ¦) welcome. ⢠Assignments are theoretical/m
mate query answering for ad hoc queries of large data warehouses GM98]. In .... When no synopsis data structure is maintained, then the best methods for.
Textbook: Discrete Math w/Applications, 4th ed. by Susanna Epp. Course
description (UVM ... statement of the question (copy the question from the book)
and your solution. Homework is .... (See BlackBoard for assignment .pdf). Thur,
Oct 9.
... and bene�ts Download free C eBooks in pdf format or read C books online ... Top 5 Data Structure and Algorithm B
Apr 13, 2001 ... In practice you need programming experi- ... Data Structures and Program
Design in C, Second Edition, Kruse, Leung, and Tondo, Prentice-.
Acknowledgments. Thanks to the following individuals for finding many of the
errors in earlier editions: Dan Bonachea, Michael Clancy, Dennis Hall, Yina Jin,.
Office hours: Tuesdays 6:00 – 8:00 p.m. in CSB 300 (main lab) or by ... Given a
problem, define the data structures that are required to solve it efficiently. 2.
Office: COMP 207. Office Hours: MW 2:00-3:00 and by appointment. CS 2401:
Elementary Data. Structures and Algorithms http://www.cs.utep.edu/vladik/cs2401
.
summaries for two promising storage methods: the grid file and partitioned hashing. We show ... central problem in broker design is to find a representation for summary ... query. Second, we suggest appropriate data structures for storing such.
Data Structures and Algorithm Analysis in Java, by Mark Allen Weiss. Addison
Wesley ... fundamental data structures and analysis and design of algorithms.
Program assignments are due 1 week after being assigned unless otherwise stated in class. Some work may be in small grou
Instructor: Bruce McMillin. Rm 322 Computer Science Building. 341-6435, [email protected]. Office Hours: Monday. 08:30-10:30. W
Data Structures and Problem Solving Using C++, Weiss, 2nd Ed. ... Course
Description: A course covering classic data structures and an introduction to
object-.
Data Structures and Algorithm Analysis in Java, by Mark Allen Weiss. ...
laboratory assignments will be drawn from Data Structures and Algorithm
Analysis in.
Textbook. Data Structures and Algorithm Analysis in Java, by Mark Allen Weiss.
... c. lists implemented as arrays or linked lists. Level 2: Application and Analysis:.
Data Structures and Algorithm Analysis in Java, by Mark Allen Weiss. ... Describe,
implement, and use the following data structures: a. Heaps b. Hash tables c.
Arnon Amir* Alon Efratâ Piotr IndykÐ¥ Hanan Sameto. Abstract. In this paper we investigate data-structures obtained by a recursive partitioning of the input ...
28 Aug 2013 ... This course introduces fundamental concepts in data structures and ... help you
learn Java on your own: Deitel and Deitel, Advanced Java 2 ...
CS 315 Data Structures Practice Questions for mid-semester # 2. (Date of the test
: November 13, duration: 75 minutes). 1) Exercises 5.1, 5.2. 3) Write a member ...
Introduction: why we need clever algorithms and data structures. • Performance
and Big O: how much time and storage is needed as data sizes become large.
1. Classic data structure used to implement priority queues. 2. Memory space
used for dynamic alloca?on. We will study the data structure (not dynamic
memory.
CS261 Data Structures
CS 261 – Data Structures Priority Queue ADT & Heaps
Goals • Introduce the Priority Queue ADT • Heap Data Structure Concepts
Priority Queue ADT • Not really a FIFO queue – misnomer!! • Associates a “priority” with each element in the collecGon: – First element has the highest priority (typically, lowest value)
• ApplicaGons of priority queues: – To do list with prioriGes – AcGve processes in an OS
Priority Queue ADT: Interface • Next element returned has highest priority void add(newValue); TYPE getMin(); void removeMin();
Priority Queue ADT: ImplementaGon Heap: has 2 completely different meanings 1. Classic data structure used to implement priority queues 2. Memory space used for dynamic allocaGon We will study the data structure (not dynamic memory allocaGon)
Priority Queue ADT: ImplementaGon Binary Heap data structure: a complete binary tree in
which every node’s value is less than or equal to the values of its children (min heap)
Review: a complete binary tree is a tree in which 1. Every node has at most two children (binary) 2. The tree is enGrely filled except for the boYom level which is filled from leZ to right (complete) – Longest path is ceiling(log n) for n nodes
Min-‐Heap: Example Root = Smallest element
2
3
5
9
14
10
12
11
Last filled posiGon
7
16
(not necessarily the last element added)
8
Next open spot
Maintaining the Heap: AddiGon 2
Add element: 4 3
5
9
14
10
12
11
7
16
4
8 New element in next open spot.
Place new element in next available posiGon, then fix it by “percolaGng up”
Maintaining the Heap: AddiGon (cont.) 2 3
5
9 14
10 12
11
4 16
2
8 3
7
4
AZer first iteraGon (swapped with 7) 9 14
10
swap value with parent
11
16
8
7 AZer second iteraGon (swapped with 5) PercolaGng up: New value not less than parent à Done while new value is less than parent,
12
5
Maintaining the Heap: Removal • Since each node’s value is less than or equal to the values of its children, the root is always the smallest element • Thus, the operaGons getMin and removeMin access and remove the root node, respecGvely • Heap removal (removeMin): What do we replace the root node with? Hint: How do we maintain the completeness of the tree?
Maintaining the Heap: Removal Heap removal (removeMin): 1. Replace root with the element in the last filled posiGon 2. Fix heap by “percolaGng down”
Maintaining the Heap: Removal removeMin :
2
1. Move element in last filled pos into root 2. Percolate down
Root = Smallest element
3
5
9
14
10
12
11
7
16
Last filled posiGon
8
Maintaining the Heap: Removal (cont.) 16 3
5
9 14
10 12
7
3
8 16
11
5
Root value removed
(16 copied to root and last node removed)
9
14
PercolaGng down:
while greater than smallest child swap with smallest child
10 12
7
11
AZer first iteraGon (swapped with 3)
8
Maintaining the Heap: Removal (cont.) 3 9
5
16 14
10 12
7
3
8 9
11
5
AZer second iteraGon (moved 9 up) 12 14
PercolaGng down:
while greater than smallest child swap with smallest child
10 16
7
8
11
AZer third iteraGon (moved 12 up)
Reached leaf node à Stop percolaGng
Maintaining the Heap: Removal (cont.) 3
Root = New smallest element
9
5
12
14
10
16
11
New last filled posiGon
7
8
PracGce Insert the following numbers into a min-‐heap in the order given: 54, 13, 32, 42, 52, 12, 6, 28, 73, 36
PracGce Remove the minimum value from the min-‐heap 5