syllabus.txt

of ⁨6⁩

Introduction to Analysis of Algorithms
CS 4820/5820 Spring 2022 Syllabus
General Information
• Instructor: Eshan Chattopadhyay, Gates Hall 319, eshan@cs.cornell.edu.
Office hours: Monday 10:30am-11:30am, Thursday 1:30pm-2:30pm.
• Lectures: MWF 9:05am-9:55am, Uris Hall G01.
Course Description
This course develops techniques used in the design and analysis of algorithms, with an empha-
sis on problems arising in computing applications. Example applications are drawn from sys-
tems and networks, artificial intelligence, computer vision, data mining, and computational bi-
ology. This course covers four major algorithm design techniques (greedy algorithms, divide and
conquer, dynamic programming, and network flow), computability theory focusing on undecid-
ability, computational complexity focusing on NP-completeness, and algorithmic techniques for
intractable problems, including identification of structured special cases, approximation algo-
rithms, and local search heuristics. This course continues to build on work in previous courses
on proofwriting and asymptotic runtime analysis of algorithms.
Learning Objectives
On completing this course, students should be able to:
• Identify problems solvable with a greedy algorithm, design and prove the correctness of
such an algorithm, and supply asymptotic running time for a variety of given algorithms.
• Recognize problems to which divide and conquer or dynamic programming approaches
may apply, design algorithms with these approaches, and analyze their computational ef-
ficiency;
• Apply randomization to produce tractable algorithms for several specific computationally
challenging problems;
• Reduce resource management as well as partition problems to network flow or cut prob-
lems, implement correct strategies for finding optimal flows/cuts, and understand the
properties of these strategies;
• Recognize whether or not certain problems are computationally intractible (e.g. NP-complete,
uncomputable), and use reductions to known problems to prove intractability;
1
• Use approximation algorithms to efficiently produce near-optimal solutions for intractable
problems, and bound how close these algorithms are to being optimal;
• Use online algorithms to produce near-optimal solutions when only partial information
about a problem is available, and bound how close these algorithms are to being optimal;
and
• Be able to recognize, implement, and understand the properties of several famous and im-
portant algorithms including
– Gale-Shapley method for stable matchings,
– Prim’s and Kruskal’s algorithms for finding minimum spanning trees,
– Bellman-Ford’s algorithm for finding shortest paths in a graph, and
– Ford-Fulkerson’s algorithm for finding max flows in networks.
Course Material
The textbook for the course is Algorithm Design by Jon Kleinberg and Eva Tardos (available at Cor-
nell Store). Although this book was designed for this course, there will be topics covered in lecture
that are not in the text and there will be topics in the text that are not covered in lecture. You
are responsible for topics covered in lecture and for any assigned reading in the text.
The following books are also useful references.
• T. Cormen, C. Leiserson, R. Rivest. Introduction to Algorithms.
• S. Dasgupta, C. Papadimitriou, and U. Vazirani. Algorithms.
• A. Aho, J. Hopcroft, J. Ullman. The Design and Analysis of Computer Algorithms.
• M. Garey and D. Johnson. Computers and Intractability.
• D. Kozen. The Design and Analysis of Algorithms.
Prerequisites
The prerequisites for the course are, either having an A- or better in both CS 2800 and CS 2110,
or having successfully completed all three of CS 2800, CS 2110, and CS 3110. We assume that
everyone is familiar with the material in CS 2110, CS 3110, and CS 2800, and we will use it as nec-
essary in CS 4820. This includes elementary data structures, probability (conditional probability,
expectation, variance), sorting, and basic terminology involving graphs (including the concepts
of depth-first search and breadth-first search), and coding (in Java, or one of the other languages
supported – see Programming Assignment Instructions). Some of these are reviewed in the text.
The lectures and homework involve the analysis of algorithms at a fairly mathematical level. A
few of the homework exercises consist of writing code in Java. We expect everyone to be com-
fortable reading and writing proofs at the level of CS 2800, as well as writing code in Java.
2