Csci 231: The Design and Analysis of Algorithms

Fall 2009: Tu, Th 11:30 - 12:55 in Searles 126

This course is an introduction to the design and analysis of algorithms, building on the concepts from Data Structures (csci 210).

• It introduces a variety of fundamental problems like searching, sorting and selection, graphs and optimization.
• It discusses a variery of solutions to these problems, while illustrating design techniques such as divide-and-conquer, dynamic programming, greedy approach.
• It discusses methods for proving correctness and analysing the efficiency of these solutions.

It is a theoretical class, and involves no programming.

Prerequisites: Csci 210 and (Csci 189 or Math 200). The goal of the Math prerequisite is to ensure that the students have achieved a certain mathematical maturity, and a dexterity with induction and proofs. When there are no good reasons to postpone Algorithms for another year, we let students take Algorithms and Math 200 at the same time. Occasionaly it is possible to substitute Math200 with a different 200-level math class (if we weel that the mathematical background of the student is good enough).

Instructor: Laura Toma
Office: 219 Searles Hall
Email: ltoma at bowdoin
Office hours: Tu, Th 2-4pm.

In addition to the official office hours, you can drop by for quick questions any time I am in the office.

I often work late evenings and I may be in the office the evening before homework is due, but do not take it for granted and do not rely on it. When I work late, and you see my door open, feel free to drop by to ask questions or chat (As you know, when one works, digressions are very welcome).

TA: Andrew (Drew) Kantor

Drew will have weekly office hours, which he will post soon.

Class webpage: http://www.bowdoin.edu/~ltoma/teaching/cs231/fall09/. Note that this is a link from my personal website at Bowdoin. This site will contain all class-related material along the semester. The class does not have a Blackboard site.

Main Topics:

• Mathematical foundation (growth of functions, summations, recurrences)
• Sorting algorithms (insertion sort, mergesort, quicksort, heapsort, bucket sort, radix sort)
• Searching and data structures (binary search trees, red-black trees, augmented search trees, skip lists)
• Priority queues (binary heap)
• Amortized Analysis (splay trees)
• Paradigms (divide-and-conquer, greedy, dynamic programming)
• Graph Algorithms (traversal, minimum spanning tree, shortest paths)

Textbook:

• Cormen, Leiserson, Rivest and Stein, Introduction to Algorithms, 2nd Edition, McGraw Hill, New York, 1990. (bugs).

Other good reference books include:

• Kleinberg and Tardos, Algorithms.
• Skienna, The algorithm design manual.

Grading policy

• Homework assignments (approx. 30%).
• Two in-class exams (20% each) and a final exam (30%).

Homework

Homework assignments will be made available online as the semester progresses. They will usualy be due one week after they are assigned, and solutions will be handed out the day the homework is due. Because of this no credit is given for homework received late. For special situations contact me in advance.

Homework policy:

• Write your name on each sheet.
• Write the solutions in the space provided.
• Collaboration is allowed, even encouraged, provided that it helps you study better. Don't overdo it, and remember that exams are individual! Even if you collaborate with others, you must write up the solutions individually, and list the names of the collaborators.
• Homework should be submitted at the beginning of class on the due date.
• It is a good experience to try typing the solutions, at least to some problems, given that it will motivate you to be concise and clear. If it does not, I prefer handwriting.

Make-up classes

There will be a few times throughout the semester when I will need to skip a class because of conflicting engagements (conferences, committees, etc). I will inform you of the exact dates and we will schedule make-up classes ahead of time.