# Complexity Theory

- SoSe-2022
- Course codes: B-GeA-12→KTH12 · M-KLOG-12-K→KTH12
- External links: qis

### Overview

## Description

Complexity Theory is a beautiful field that studies different models of computation as well as their relationships to each other. The most famous open problem in this field is the P vs. NP problem. In this course, you will discover elegant proof techniques, fascinating ideas, and exciting open questions.

## Time and Place

**Lectures.**Tue 12-14, Wed 10-12 in H9.**Tutorials.**Tue 10-12 in H9.

## Grading

- There will be 3-4 mandatory written assignments; you can solve them in teams of size 2.
- The
**written exam**will take place on Tuesday, July 19 at 10-13 in H10.

## Week plan

The summer term has 14 weeks, the following is a preliminary plan:

✓

**Introduction: Efficient universal Turing machine.**[AB 1.2-1.4, 1.6-1.7]

In-class exercises: Example 1.1, Claims 1.5 and 1.6.

Homework: Exercises 1.5, 1.6*, 1.9, 1.10, 1.15. (* = hard, but well worth the effort)✓

**NP and NP completeness**[AB ch. 2.1-2.3, 2.5-2.7]

Homework: 2.10, 2.8, 2.25, 2.29, 2.6b*, 2.30*✓

**Diagonalization**[AB, ch. 3]

Homework: 3.5, 3.6, 3.7*, 3.8*✓

**Space complexity**[AB, ch. 4]

Homework: 4.1, 4.2, 4.3, 4.7

Written assignment: pdf✓

**The polynomial hierarchy and alternations**[AB, ch.5]

Homework: 5.3, 5.7, 5.10, 5.13✓

**Boolean circuits**[AB, ch. 6]

Homework: 6.3, 6.8, 6.9*, 6.12✓

**Randomized computation**[AB, ch.7]

Homework: 7.4, 7.7, 7.9

Written assignment: pdf. Please consider handing in the assignment as a team of size 2.✓

**Interactive proofs**[AB, ch. 8.1-8.3]

Homework: 8.1, 8.2, 8.6*✓

**PCP Theorem**[AB, ch. 11]

Homework: Read!, 11.2, 11.3, 11.6, 11.9*, 11.15✓

**Decision Trees**[AB, ch. 12]

Homework: 12.1, 12.2, 12.3, 12.6✓

**Complexity of Counting**[AB, ch. 17] (this week happening via Zoom!)

Homework: 17.2, 17.4, 17.6*✓ Self-study: Third and final written assignment: pdf. Please consider handing in the assignment as a team of size 2.

✓

**⊕ ∉ AC0**[AB, ch. 14.1]✓

**Recap**

## Prerequisites

This course is available for Bachelor *and* Master students!
You must have mastered Algorithms and Data Structures 1 and 2 (or equivalent) and you must love mathematics.

## Contents

- Complexity classes: Log-space, NP, BPP, polynomial-time hierarchy, ACC0, EXP, NEXP, NP/poly, #P, etc.
- Karp-Lipton’s Theorem, Hardness vs. Randomness, NEXP ⊈ ACC0, PCP theorem, etc.

## Literature

**AB**: *Computational Complexity: A Modern Approach* by Sanjeev Arora and Boaz Barak.

[Volltext als E-Book].

There is also a pdf on the book website, but be aware that it is not a final draft and has different chapter numbers.

## Further resources

- Similar Courses:
- Dieter van Melkebeek’s Complexity Theory at UW Madison.
- Ryan Williams’ Automata, Computability, and Complexity Theory and Advanced Complexity Theory at MIT.

- For background reading on Turing machines, see:
- video lecture on chapter 6 in Jeff Erickson’s Models of Computation course.
- video lectures in Michael Sipser’s course at MIT.

- Also see TCS @ Princeton.