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Henry S. Baird    Spring 2010 Course

Design & Analysis of Algorithms

CSE/Math 340     CRNs:  11264 (CSE); 13275 (Math)

Algorithms are methods for solving information processing problems. For a method to be called an algorithm, it must be fully automatable---for example, a computer can run it---and provably correct---that is, it must find the right answer for every instance of the problem.  We also often want it to run as fast as possible, and use as little memory as possible, even on huge instances.  Fast algorithms for hard, practically important problems are among the key discoveries of computer science research.  This course presents algorithms for searching, sorting, manipulating graphs and trees, scheduling tasks, finding shortest paths, matching patterns in strings, etc---and gives proofs of their correctness and analysis of their runtime and memory demands. General strategies for designing algorithms---e.g. recursion, divide-and-conquer, greediness, dynamic programming---are stressed. Limits on algorithm efficiency are explored through elementary NP-completeness theory.

Prerequisites:  Calculus II (Math 22 or Math 32) and Discrete Structures (CSE/Math 261); or close equivalents (in which case, check with instructor).  To review your Discrete Structures background, look at the textbook's Appendix sections A.1-2, B.1-3,  C.1., & D.1.

This is a required (core) course for these undergraduate degree programs: B.S. in CS (in RCEAS); B.A. in CS (in CAS); and the B.S. in Computer Science & Business (in RCEAS & CBE).

(There is a graduate-level version of this course:   CSE 498 Advanced Algorithms.  No student may take both CSE/Math 340 and CSE 498 for credit. CSE 498 is designed to be an effective preparation for the CS Ph.D. Algorithms Qualifier Exam, in addition to awarding graduate-level credit; CS PhD students are very welcome to "sit in on" this course without registering for it or formally auditing it.)

Course objective:  On completing this course, students will be sufficiently familiar with the theory, practice, notation, and vocabulary of algorithm design and analysis to be able to locate in the literature (or design from scratch) provably correct and---to the extent possible---efficient algorithms to solve a wide range of problems. They will understand how to judge whether or not a new problem is likely to have an efficient algorithm.  They will also have a grasp of basic engineering issues arising in the implementation, adaptation, and application of algorithms.

Textbook:  T. Cormen, C. Leiserson, R. Rivest, & C. Stein, Introduction to Algorithms, 3rd Edition, The MIT Press,  Cambridge, Massachusetts, 2009 (ISBN 978-0-262-03384-8 hardcover, or 978-0-262-53305-8 paperback).  You do not need to buy the companion Java CD-ROM.  A desk copy may be consulted in the CSE Dept office PL 354 (but cannot be taken away). Another extra copy is available in Dr. Baird's office (PL514C), when he's there; but, again, it can't be taken away.

We follow the textbook closely.  Lectures may discuss only the most important topics in a section of the textbook, but students are expected to study the corresponding section completely. Unless stated otherwise, all material in covered sections is relevant to homework and tests.

Classroom lectures:  MWF 11:10 AM - 12:00 PM in Packard Lab 416 (PL 416). 

Attendance and Quizes: Attendance in class is not required.  There may be, however, Pop Quizes, and a missing quiz results in zero points.

Homework:  There are weekly written homework assignments, due normally on Friday morning, to be handed in as hardcopy at the start of the class. For part of each Friday's class, students are invited to present their solutions on the blackboard, for which they will receive 5 points credit (whether or not their solution is correct); this 'presentation' credit is added to the HW+Quiz score, up to a fixed maximum---and so can make up for points lost.

Exams:  There are two hour exams and a final 3-hour exam:  all are closed-book, in-class, written exams.   Exams are not repeated: if under extraordinary circumstances a student cannot take an exam, it is assigned the average grade of the other two exams.

Grading: 80% Exams (20% 1st hour exam, 20% 2nd hour exam, 40% final exam); 20% Homeworks + Quizzes + Presentations.

Instructor:  Henry Baird, Prof., CSE Dept, hsb2@lehigh.edu. Office:  Packard Lab 514C.   Office Hours:  Wednesdays 12:00-1:00 PM, or by appointment.

Grader: Ulit Jaidee, ulj208@lehigh.edu.  Office Hours: Wednesdays 1:00-2:00 PM, in PL 514B, or by appointment.

Course Site:  CSE-340-010-SP10 Design & Analysis of Algorithms. We will use the on-line Course Site system to email announcements & distribute lecture notes, homeworks, grades, etc. Once you enroll in the course, browse coursesite.lehigh.edu, login using your Lehigh id and password, and you should see that you have access to this course. If you can't login, email the instructor immediately.

Accommodations for Students with Disabilities:  If you have a disability for which you are or may be requesting accommodations, please contact both your instructor and the Office of Academic Support Services, University Center 212 (610-758-4152) as early as possible in the semester.  You must have documentation from the Academic Support Services office before accommodations can be granted.

Topics Covered (probable; chapters in textbook):
Algorithm Basics:
   Definitions, Properties (Ch. 1)

   Algorithm Performance Analysis (Ch. 2)
       Insertion Sort & MergeSort

   Growth of Functions (Ch. 3)
   Divide and Conquer (Ch, 4)
       Recurrences & their Solution

   Heapsort, Priority Queues (Ch. 6)
   Quicksort (Ch. 7)
   Lower Bounds, Sorting in Linear Time (Ch. 8)
Data Structures:
   Stacks, queues, lists, graphs, trees (Ch. 10; review)
   Dictionaries; Hashing (Ch. 11)
   Disjoint-Sets Union/Find (Ch. 21)
Graph Algorithms:
   Bread-first & Depth-first Search (Ch. 22)
       Topological Sort
   Shortest Paths:  Bellman-Ford, Dijkstra (Ch. 24)
Greedy Algorithms:
Huffman Trees (Ch. 16)
   Minimum Spanning Trees:  Kruskal (Ch. 23)
Dynamic Programming:

   Matrix-chain multiplication (Ch. 15)
   All-Pairs Shortest Paths:  Floyd-Warshall (Ch. 25)
P, NP, NP-completeness:
   The Complexity Classes P & NP (Ch. 34)
       Polynomial-time Reductions among Problems
       NP-complete Problems, P=NP?
   Approximation Algorithms (Ch. 35)
Miscellaneous (as time permits):
   String Matching:  KMP, Rabin-Karp (Ch. 32)
   Computational Geometry:  Convex Hull (Section 33.3)
   Polynomials & the Fast Fourier Transform (Ch. 30)

If you have any questions, ask the instructor: hsb2@lehigh.edu.


© 2003 P.C. Rossin College of Engineering & Applied Science
Computer Science & Engineering, Packard Laboratory, Lehigh University, Bethlehem PA 18015