Algorithms: Design and Analysis, Part 2

In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.

About The Course

In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments.  You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer.  Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics.  Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!

Frequently Asked Questions

  • Will I get a statement of accomplishment after completing this class? Yes. Students who successfully complete the class will receive a statement of accomplishment signed by the instructor.

  • How does Algorithms: Design and Analysis differ from the Princeton University algorithms course?

    The two courses are complementary. That one emphasizes implementation and testing; this one focuses on algorithm design paradigms and relevant mathematical models for analysis. In a typical computer science curriculum, a course like this one is taken by juniors and seniors, and a course like that one is taken by first- and second-year students.

Recommended Background

How to program in at least one programming language (like C, Java, or Python); and familiarity with proofs, including proofs by induction and by contradiction.  At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.  The course assumes familiarity with some of the topics from Algo 1 --- especially asymptotic analysis, basic data structures, and basic graph algorithms.