CS 672: Complexity Measures for Boolean Functions
Announcements
- The endsem is on 18th Nov from 0800-10:00 AM in L11 (OROS).
Course Notes
Topic | Link |
Introduction to Boolean functions | Introduction |
Polynomial representation and properties | Fourier analysis |
Influence, stability and social choice theory | Influence |
Decision tree complexity, randomized and quantum query complexity | Decision tree |
Approximate degree, lower bounds on quantum query | Approximate degree |
Lower bound on randomized query complexity, Yao's minimax | Lower bounds |
Sensitivity and other complexity measures | Sensitivity |
Proof of Sensitivity Conjecture: Huang | Huang's result |
Communication complexity and lower bounds | Communication |
Administrative details:
- Time: 10:30-11:45 Mon/Thu.
- First course handout
- Anti-cheating policy: from CSE Dept
- Drop policy: from DUGC
- TA: Yugal Pandey(yugulk24 at iitk)
- Grading:
- Assignment (1*5 = 5)
- Quiz (2*10 = 20)
- Midsem (25)
- Project (5+15+20)
- Endsem (10)
Course description:
The analysis of Boolean functions has been an important tool in
many diverse areas of computer science, e.g., complexity theory, property testing,
social choice theory and quantum computing. The aim of this course is to
provide an introduction to the analysis of Boolean functions, different complexity
measures on these Boolean functions and relationship between these complexity
measures.
We will start with Fourier analysis on the Boolean hypercube, how it is defined and what are its important properties. These properties will be accompanied by applications in different areas of computer science. We will cover the concept of Fourier degree and approximate degree too. This back-ground material and applications will constitute around half of the course.
The remaining half will be composed of different complexity measures on Boolean functions and the relationship between them. We will cover mathematical complexity measures on Boolean functions like sensitivity, degree and certficate complexity. Other complexity measures coming from the computational world like decision tree complexity, randomized and quantum query complexity will also be discussed. The course will also deal with relations known between these complexity measures from different domains. Most of these were polynomially related and we will give proof of these relationships. If time permits, we will cover the recent breakthrough result of Huang which shows that even sensitivity is polynomially related to other complexity measures.
References
Boolean Functions
- Analysis of Boolean functions, Ryan O'Donnell.
- Complexity Measures and Decision Tree Complexity: A Survey, Harry Buhrman and Ronald de Wolf.
- A Brief Introduction to Fourier Analysis on the Boolean Cube, Ronald de Wolf.
Linear Algebra
- Linear Algebra and its Applications, G. Strang.
- Matrix Analysis, Bhatia.