Course Identification

Hardness of Approximation
20194212

Lecturers and Teaching Assistants

Prof. Irit Dinur, Dr. Amey Bhangale
N/A

Course Schedule and Location

2019
Second Semester
Thursday, 14:15 - 16:00, Jacob Ziskind Building, room 208
04/04/2019

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; 2.00 points

Comments

Two additional Sunday sessions (2:30--4:30) on May 12 and May 26 in math 2 room 108.

Prerequisites

No

Restrictions

25

Language of Instruction

English

Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

30%
35%
35%

Evaluation Type

Seminar

Scheduled date 1

N/A
N/A
-
N/A

Estimated Weekly Independent Workload (in hours)

1

Syllabus

We will go over some recent progress in hardness of approximation, focusing on the unique games conjecture.

We will begin with the "label-cover long-code paradigm" of proving tight inapproximability results, describing Hastad's celebrated results for 3SAT, for linear equations, and for maximum clique.

We will then move to show implications of the unique games conjecture for hardness of approximation, for example for finding the maximum cut in a graph and for essentially every constraint satisfaction problem.

Then, we will proceed to read newer works that make progress on proving the unique games conjecture and the related 2:1 conjecture.

 

The tentative list of topics is as follows:

 

Lecture 1:

1. Introduction: Constraint satisfaction problems, PCP theorem, Property testing

2. Basics: Boolean functions, Fourier analysis, Influence etc.

3. BLR Linearity Test

Lecture 2:

4. PCP Theorem implies gap3LIN(0.99, 1-\epsilon)

5. Parallel Repetition, Subcode covering, “linear” Label Cover instance

Lecture 3:

6. Hastad’s 3-bit PCP, Graph/Hypergraph Linearity Tests, amortized k-bit PCP

Lecture 4:

7. Majority is the Stablest Theorem, Invariance Principle

8. Unique Games Conjecture, Max-Cut algorithm and UG-hardness

Lecture 5:

9. (UGC + Hypergraph Linearity Test)

10. Chan’s Approximation resistance, Invariance Principle

Lecture 6:

11. FGLSS reduction, free bits, independent set

Lecture 7-8:

12. 2-to-1 conjecture, Grassmann graph, agreement hypothesis

13. 2-to-1 Theorem with imperfect completeness

Lecture 9:

14. Small set expansion hypothesis, Short Code.

Lecture 10:

15. Other hardness assumptions: Feige’s random 3SAT hypothesis, ETH, Planted Clique.

 

Learning Outcomes

The student will have understanding of the state of the art in hardness of approximation

Reading List

N/A

Website