CS2013-final-report

COS 226: Algorithms and Data Structures, Princeton University
Princeton, NJ
Robert Sedgewick and Kevin Wayne
rs@cs.princeton.edu wayne@cs.princeton.edu
http://www.cs.princeton.edu/courses/archive/spring12/cos226/info.php
http://algs4.cs.princeton.edu
Knowledge Areas that contain topics and learning outcomes covered in the course
Knowledge Area
Total Hours of Coverage
Algorithms and Complexity (AL) 29
Software Development Fundamentals (SDF) 3
Programming Languages (PL) 1
Where does the course fit in your curriculum
This course introduces fundamental algorithms in the context of significant applications in science, engineering,
and commerce. It has evolved from a traditional “second course for CS majors” to a course that is taken by over
one-third of all Princeton students. The prerequisite is a one-semester course in programming, preferably in Java
and preferably at the college level. Our students nearly all fulfill the prerequisite with our introductory course. This
course is a prerequisite for all later courses in computer science, but is taken by many students in other fields of
science and engineering (only about one-quarter of the students in the course are majors).
What is covered in the course
Classical algorithms and data structures, with an emphasis on implementing them in modern programming
environments, and using them to solve real-world problems. Particular emphasis is given to algorithms for sorting,
searching, string processing, and graph algorithms. Fundamental algorithms in a number of other areas are covered
as well, including geometric algorithms and some algorithms from operations research. The course concentrates on
developing implementations, understanding their performance characteristics, and estimating their potential
effectiveness in applications.
• Analysis of algorithms, with an emphasis on using the scientific method to validate hypotheses about
algorithm performance.
• Data types, APIs, encapsulation.
• Linked data structures, resizing arrays, and implementations of container types such as stacks and queues.
• Sorting algorithms, including insertion sort, selection sort, shellsort, mergesort, randomized quicksort,
heapsort.
• Priority queue data types and implementations, including binary heaps.
• Symbol table data types and implementations (searching algorithms), including binary search trees, redblack
trees, and hash tables.
• Geometric algorithms (searching in point sets and intersection).
• Graph algorithms (breadth-first search, depth-first search, MST, shortest paths, topological sort, strong
components, maxflow)
• Tries, string sorting, substring search, regular expression pattern matching.
• Data compression (Huffman, LZW).
• Reductions, combinatorial search, P vs. NP, and NP-completeness.
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