# Instructor: Dr. Sahar Shabanah Fall 2010. Lectures ST, 9:30 pm-11:00 pm Text book: M. T. Goodrich and R. Tamassia, “Data Structures and Algorithms in.

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Instructor: Dr. Sahar Shabanah Fall 2010

Lectures ST, 9:30 pm-11:00 pm Text book: M. T. Goodrich and R. Tamassia, “Data Structures and Algorithms in Java”, 4 th Edition, 2005, Wiley, ISBN: 978-0471738848 Lecture slides will be posted on the course page before each lecture. Read thru the lecture notes and the assigned readings before class. Be prepared to ask questions. Class website: http://groups.yahoo.com/group/CPCS204_F10/

Grading 20% Lab & Assignments 20% Mid-Term Exam 20% Final Project 40% Final exam

Course Content Object Oriented Design Arrays & Linked Lists Analysis tools Stacks & Queues Lists Trees Heaps Maps & Tables Sorting & Searching Algorithms

Data Structures A data structure in computer science is a way of storing data to be used efficiently. A data structure is a representation of a finite data set [2]. Data Structures examples are Array, List, Linked list, Doubly linked list, Stack, Queue, Hash table, Graph, Heap, Tree, Binary Search tree, Red-Black tree, etc

Data Structure Basic Operations Queries operations get information about the data structure. Search (data structure, key): searches for a given key in a data structure. Sort (data structure): sorts elements of a data structure. Minimum(datastructure): finds the element with the minimum value in a data structure.

Data Structure Basic Operations Maximum (data structure): finds the element with the maximum value in a data structure. Successor (data structure, element): finds the element that succeeds the given element in a data structure. Predecessor (data structure, element): finds the element that precedes the given element in a data structure.

Data Structures Basic Operations Modifying operations: Change the status of a data structure. Insert (data structure, element): inserts an element into a data structure. Delete (data structure, element): deletes an element from a data structure.

Algorithms An algorithm is a sequence of computational steps that transform the input into the output. Algorithms can be classified according to the problem-solving approach that they use or the problems that they solve.

Algorithms with similar problem- solving approach Recursive Algorithms: convert the problem into sub-problems, then solve each one using recursion. Backtracking Algorithms: return a solution if found or recur through the problem with every possible choice until solution or failure is reached. Brute Force Algorithms: try all possibilities until a satisfactory solution is found.

Algorithms with similar problem- solving approach Divide and Conquer Algorithms: divide the problem into smaller sub-problems of the same type, and solve these sub-problems recursively, then combine the solutions to the sub-problems into a solution to the original problem. Dynamic Programming Algorithms: find the best solution of multiple exist solutions. Examples are Knapsack and Activity Selection Problem. Brute Force Algorithms: try all possibilities until a satisfactory solution is found.

Algorithms with similar problem- solving approach Greedy Algorithms: get the best solution at the moment, without regard for future consequences. By choosing a local optimum at each step, it will end up at a global optimum. Examples are Prim’s and Dijkstra’s algorithms. Branch and Bound Algorithms: a tree of sub-problems is formed. Randomized Algorithms: use a random number at least once during the computation to make a decision.

Algorithms solve similar problems Sorting Algorithms: Bubble Sort, Selection Sort, Insertion Sort, Shell Sort, Merge Sort, Heap Sort, Quick Sort, Bucket Sort, etc. Linear-Time Sorting: Counting Sort, Radix Sort, Bucket Sort, etc. Graph Algorithms: Breadth First Search (Bfs), Depth First Search (Dfs), Topological Sort, Strongly Connected Components, Generic Minimum Spanning Tree, Kruskal’S, Prim’S, Sin- gle Source Shortest Path, Dijkstra’S, etc.

Algorithms solve similar problems Searching Algorithms: List Search: Linear Search, Binary Search, etc. Tree Search: Breadth First Search, Depth First Search, etc. Informed Search: Best-First Search, A*, etc. String Matching: Naïve String Matching, Knuth- Morris-Pratt, Boyer-Moore, etc.

Java Programming Basics Base Types: Objects Enum Types Methods Expressions Control flow Arrays Simple Input and Output

Object-Oriented Design Intro Inheritance Polymorphism Exceptions Interfaces and abstract Classes Casting Generics

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