W 5-6:50, Room 101
M 5-5:50 Room 101
Office Hours: M: 6-7,W: 7-8.
The Mondays session is for recitation;
Recitation attendance is Highly Recommended
Office Hours: M: 6-7,W: 7-8.
This course covers the design and analysis of
combinatorial algorithms. The curriculum is concept-based and emphasizes the
art of problem-solving. The class features
weekly exercises designed to strengthen conceptual understanding and problem
solving skills. Students are presumed to have adequate programming skills and
to have a solid understanding of basic data structures and their implementation
in the programming languages of their choice. Although some mathematical
sophistication is very helpful for this course, the necessary mathematics is
contained within the curriculum.
Because of the emphasis on problem solving, students are
expected to attend the Monday
recitation sessions, where sophisticated concepts will be reviewed and illustrated in depth.
Algorithmic Design Paradigms
The Greedy Method
Sorting- and Selection-based processing
Algorithm Redesign and Adaptation
The Analysis of Algorithmic Performance
The Recursion Tree Solution Method
Managing Data for Efficient Processing
Lists, Stacks, Queues, Priority Queues, Trees and
Tarjan's Categorization of Data
Search Trees and their Enhancement
Sorting, Selection, and Hashing
Selected Representative Algorithms/problems
Biconnected Components and Strong Components
Representative styles of Dynamic Programming and their applications
Standard Sorting and Selection Algorithms
Selected topics in Hashing
Minimum Spanning Trees
Shortest Path Problems
Inside Guide to Algorithms: their application, adaptation, design and analysis,
by A.R. Siegel and R.J.
Cole. A new edition is in the works, and will be available soon.
There will be approximately 11 written homework assignments
that have, on average, about 10 exercises each. Perhaps one-third
to one-half of these problems will be extremely challenging. That is,
the necessary concepts will have already been taught, but a good deal
of thought will be needed to figure out how to apply these techniques
to solve the more challenging exercises.
- Students are not required to solve even the majority of the difficult
but they are expected to write down what ideas/methods they used, and where
their solution method seems to have broken down.
- Students are also expected to compare their own answers with the
solution handouts to see what concepts and techniques were overlooked.
Because more than a third
of the course is embedded in the exercises, students are expected to study the
answers as a vehicle for mastering the material.
- Homework will receive two grades: overall performance, and quality of
effort. Incorrect and even fragmentary incorrect answers can receive full
credit for the QoE grade.
Course Grading Policy
20% Midterm Exam
5% Overall homework performance grade
20% Overall QoE homework grade
47% Final Exam
8% Classroom participation