CS 401 / MCS 401 Home Page     
Spring, 2008

CS 401 / MCS 401 is an advanced undergraduate course / beginning graduate on computer algorithms.  Although many of the algorithms covered in this course are used heavily in computer programs, and some have been incorporated into programming libraries, MCS 401 / CS 401 is not itself a programming course; no programming assignments will be given.  The course covers general techniques for designing computer algorithms, as well as specific algorithms for a number of important computations (sorting, searching, shorted paths in graphs, etc.).  The objectives of the course are

• to explain and illustrate how a number of useful algorithms work,
• to prove that they do work, when that is not obvious,
• to analyze the performance (especially running time) of these algorithms, in a machine-independent way,
• to discuss the strengths and weaknesses of the algorithms, and to compare several algorithms for the same problem,
• to present general techniques for designing algorithms, and to show that many of the specific algorithms are examples of these general techniques.

Students are expected to have a reasonable knowledge of a computer language (preferably C, C++, or Java), a course on data structures, and a background in calculus, discrete mathematics, and very elementary statistics.

 

	Organization of course  General information  (textbook, exam dates, grading, etc; requires Adobe Acrobat Reader)  Detailed syllabus  (requires Adobe Acrobat Reader)
	Homework Assignments (from textbook, unless otherwise indicated) Week #1 Exercises  (due Wed, Jan 23) Week #2 Exercises  (due Fri, Feb 1) Week #3-4 Exercises  (due Mon, Feb 11) Week #5 Exercises  (due Fri, Feb 22) Week #6-7 Exercises (due Weds, Mar 5) Week #8-9 Exercises (due Weds, Mar 19) Week #10-11 Exercises (due Weds, Apr 9) Week #13-14 Exercises (due Weds, April 30)
	Homework Solutions Solutions to Week #1 Exercises Solutions to Week #2 Exercises Solutions to Week #3-4 Exercises Solutions to Week #5 Exercises Solutions to Week #6-7 Exercises Solution to Week 8-9 Exercises (P and Q only, others to be posted next week) Solutions to Week 10-11 Exercises   (not covered on Quiz 3, but may be covered on Final) Solutions to Week 13-14 Exercises (exercises V, W, X, Y)  (Use in studying for Quiz 3)
	Exams (study guides, solutions)   Final Exam Study Guide
	Handouts (require Adobe Acrobat Reader)  Fast exponentiation Straight insertion sort Rate of growth of functions Factorials Rate of growth:of exponentials, polynomials, and logarithms Summation by parts Examples of iterative and recursive algorithms Merging and Mergesort Recurrences:  Approximating floor(n/2) and ceiling(n/2) by n/2 Solving recurrences: Recurrence Trees Solving recurrences: The Master Theorem An Extension to The Master Theorem Sorting algorithm: Taking advantage of order present Sorting algorithms: Stability Nearly complete binary trees, heaps, and priority queues Heapsort Examples of decision trees Quicksort Selection (finding the kth smallest) in linear expected time: Example Selection (finding the kth smallest) in linear expected time:  The algorithm Order of Matrix Multiplication Longest Common Subsequence All-pairs Shortest Path Algorithm in Graphs Minimal Spanning Trees Prim's Algorithm and Dijkstra's Algorithm Graphs and Digraphs Breadth-first Search Depth-first Search: Examples The Depth-first Search Algorithm Applications of Depth-First Search String Matching with Finite Automata

Useful links Instructor's home page MSCS Department home page UIC home page