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# Algorithms: Design and Analysis, Part 2

Go to Course## Course Syllabus

**Weeks 1 and 2**: The greedy algorithm design paradigm. Applications to optimal caching and scheduling. Minimum spanning trees and applications to clustering. The union-find data structure. Optimal data compression.

**Weeks 3 and 4**: The dynamic programming design paradigm. Applications to the knapsack problem, sequence alignment, shortest-path routing, and optimal search trees.

**Weeks 5 and 6**: Intractable problems and what to do about them. NP-completeness and the P vs. NP question. Solvable special cases. Heuristics with provable performance guarantees. Local search. Exponential-time algorithms that beat brute-force search.## Recommended Background

## Suggested Readings

## Course Format

## FAQ

Date:

Monday, June 30, 2014 to Sunday, August 24, 2014

Course topic:

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!

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.

No specific textbook is required for the course. Much of the course material is covered by the well-known textbooks on algorithms, and the student is encouraged to consult their favorite for additional information.

The class will consist of lecture videos, generally between 10 and 15 minutes in length. These usually have integrated quiz questions. There will also be standalone homeworks and programming assignments that are not part of video lectures, and a final exam.

**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.

Instructor(s):

Tim Roughgarden