Saturday, November 18, 2017

Day 35

Shor's Algorithm - polynomial-time quantum algorithm for factoring

Wednesday, November 15, 2017

Monday, November 13, 2017

Day 33

Introduction to Quantum Computing. Gave the matrix definition of quantum machines (see my paper below) and described the Hadamard matrix and its properties.

Friday, November 10, 2017

Day 32

In this class we showed how MIP = NEXP implies lower bounds on approximating the clique problem and how this led to the PCP theorem. The PCP theorem states that every language in NP has  probabilistically checkable proof where the verifier uses O(log n) random bits and makes only a constant number of queries to a proof that just returns bit. Here is a strong version.

For any constant α > 7/8, there is a polynomial-time computable function f mapping 3-CNFs to 3-CNFs such that for all formula φ,

  • If φ is satisfiable then f(φ) is satisfiable.
  • If φ is not satisfiable then every assignment to f(φ) satisfies at most an α-fraction of the clauses.
The proof is an assignment, O(log n) random coins are used to pick a clause and only the 3-queries to the variables of that clause are needed.

We also talked about the Unique Games conjecture

Links:

Wednesday, November 8, 2017

Day 31

Assignment 3 has been graded and solutions posted on the T-Square resource page. Assignment 4 has been posted and is due November 20.

We showed that MIP (in PCP form) was equal to NEXP.

Monday, November 6, 2017

Day 30

We showed that IP = PSPACE via the Shen proof and introduced the MIP model.


Friday, November 3, 2017

Day 29

We gave the proof that co-NP and P#P have interactive protocols (Arthur-Merlin games with unbounded rounds). We will generalize to PSPACE on Monday.

Wednesday, November 1, 2017

Day 28

We proved Toda's theorem that PH is in P#P.
Along the way we needed:
  • NP in BPP⊕P (follows from Valiant-Vazirani discussed last class)
  • ⊕P⊕P = ⊕P (Papadimitrious Zachos)
  • NP in BPP implies PH in BPP (Assignment 3)
Links

Monday, October 30, 2017

Day 27

We discussed closure properties of #P functions, some basic counting classes and the proof of Valiant Vazirani.

Let f(x,y) be a #P function and r(x) a polynomial-time computable function bounded by a polynomial in |x|.
  • The sum of f(x,y) for all y with |y| = r(x) is in #P.
  • The product of f(x,y) for all y with 1 ≤ y ≤ r(x) is in #P.
  • (f(x,0) choose r(x)) is in #P.
L is in PP if there is a #P function f and a polynomial-time computable r such that
  • x in L implies f(x) ≥ r(x)
  • x not in L implies f(x) < r(x).
L is in ⊕P if there is a #P function f  such that
  • x in L implies f(x) is odd
  • x not in L implies f(x) is even
L is in UP if there is a #P function f  such that
  • x in L implies f(x) = 1
  • x not in L implies f(x) = 0
We also proved the Valiant-Vazirani theorem: There is a polynomial q and a probabilistic polynomial-time function f mapping CNF formulas to CNF formulas such that

  • If φ is not satisfiable then f(φ) is never satisfiable.
  • If φ is satisfiable then with probability at least 1/q(n), f(φ) has a unique satisfying assignment.
Link:

I also wanted to mention two upcoming talks by Joel Spencer, a great speaker and master of the probabilistic method. 

Friday, October 27, 2017

Day 26

Avi Wigderson, one of the giants of computational complexity, has released a PDF draft of his new book Mathematics and Computation. The book gives a philosophical and technical overview though a number of topics in computational complexity. Definitely worth downloading and reading if you are interested in the material in this course.

Today we proved Valiant's theorem that the permanent of a matrix is #P-complete.

Wednesday, October 25, 2017

Day 25

Showed that AM in Π2P and MA in Σ2P∩Π2P and if AM is in co-NP then PH collapses to Σ2P.

This implies that if Graph Isomorphism is NP-complete then PH collapses to Σ2P.

Introduced counting complexity, #P and the Permanent.

A function f:{0,1}*→ℕ is in #P if there is an NP machine M such that f(x) = the number of accepting paths of M(x).

The permanent of a matrix A is the sum over all permutations s of a1s(1)...ans(n).


Friday, October 20, 2017

Day 24

Assignment 2 solutions have been posted on the T-Square resources page. A reminder that Assignment 3 is now available and is due November 1.

We gave the public-coin protocol for Graph Non-Isomorphism and showed that MA is contained in AM.

  • Public-coin protocol write up by Jonathan Katz

Wednesday, October 18, 2017

Day 23

Assignment 3 has been posted on T-Square and due November 1. Assignment 2 grades have been released. Solutions to assignment 2 will be posted in the T-Square resources shortly.

Started our unit on Arthur-Merlin games.

Defined AM and gave the private-coin protocol for graph non-isomorphism and informally described the public coin protocol. We'll do a detailed public-coin protocol on Friday.

Monday, October 16, 2017

Day 22

Today we did the Schwartz-Zippel lemma and used it to give a co-RP algorithm for polynomial identity-testing.

We also showed that BPP is in P/poly and in ΣP2∪ΠP2.

Links:

Friday, October 13, 2017

Day 21

We did the proof of the Chernoff Bounds. Here is a detailed write-up.

We also discussed the polynomial identity testing question: Given a polynomial p(x1,...,xn) described by a circuit of product and sum gates, is that polynomial exactly zero?

To give a probabilistic algorithm for polynomial identity testing we looked at the Schwartz-Zippel lemma and gave part of the proof.

Wednesday, October 11, 2017

Day 20

We introduced probabilistic Turing machines and talked about the classes BPP, RP and ZPP.

A language is in BPP if there is a probabilistic polynomial-time Turing machine such that for all x,

  • If x is in L then the probability that M(x) accepts is at least 2/3.
  • If x is not in L then the probability that M(x) accepts is at most 1/3.

A language is in RP if there is a probabilistic polynomial-time Turing machine such that for all x,

  • If x is in L then the probability that M(x) accepts is at least 1/2.
  • If x is not in L then the probability that M(x) accepts is 0.
A language is in ZPP if there is a probabilistic expected polynomial-time Turing machine such that for all x,

  • If x is in L then the probability that M(x) accepts is 1.
  • If x is not in L then the probability that M(x) accepts is 0.
We sketched why Composite testing is in RP and used Chernoff bounds to reduce the error in probabilistic algorithms to exponentially small. The exact statement of Chernoff bounds I gave in class was incorrect--we'll revisit on Friday.

Links:

Friday, October 6, 2017

Day 19

Finished proof of Razborov-Smolensky. Links in Day 18 post.

Kannan's non-constructive proof that Σp2 does not have nk-size circuit for any constant k.

Links:

Wednesday, October 4, 2017

Day 18

First half of Razborov-Smolensky proof that depth d circuits of Mod3, AND, OR and NOT gates computing parity must have an exponential number of gates.

Links:

Monday, October 2, 2017

Day 17

P-complete sets including Monotone Circuit Value: Given a circuit of just AND/OR gates with variables set to the constants TRUE and FALSE, will the circuit evaluate to TRUE?

The major results in circuit complexity:
  • Parity requires exponential-size circuits. 
  • For distinct primes p and q, there is no polynomial-size constant-depth circuit with AND, OR, NOT and Modp gates that can compute the Modq function.
  • Clique and Matching do not have polynomial-size monotone circuits even with arbitrary depth.
  • *NEXP does not have constant-depth circuits with Modm gates for any fixed m, prime or composite. 
Links:

Friday, September 29, 2017

Day 16

Karp-Lipton result that if SAT has a polynomial-size circuit family then PH = Σp2.

Definitions of circuit complexity classes (NCi, ACi). NCi is the set of languages accepted by poly-size O(logi n) depth with bounded fan-in. ACi circuits are the same with no restriction on fan-in.

Links:

Wednesday, September 27, 2017

Day 15

Assignment 2 now available on the T-Square site. Due October 11.

Defined circuits, size and depth. Showed that a language has a polynomial-size circuit family if and only if L is in P/poly.

Blog post on P/poly.

Monday, September 25, 2017

Day 14

Defined quantified Boolean formula and showed that TQBF, the set of true quantified Boolean formula, is PSPACE-complete.

Assignment 1 has been graded. Average score is 62. Solutions are posted to the resources section of the T-Square site. Assignment 2 will be handed out Wednesday.

Links:

  • Alternation by Chandra, Kozen and Stockmeyer. This is the paper that showed the equivalence of alternating polynomial time and PSPACE and my other relationships.
  • Nice write-up of proof that TQBF is PSPACE-complete via Joan Feigenbaum. 

Friday, September 22, 2017

Day 13

We talked about the three definitions of the polynomial-time hierarchy, by oracles, alternation and quantification, and the basic properties.

Wednesday, September 20, 2017

Day 12

Guest lecture from Venkat.

Defined polynomial-time hierarchy in terns of oracle TMs and stated some simple facts about the hierarchy. Defined alternating Turing machines. Started the proof of the characterization of polynomial-time hierarchy using alternating Turing machines.

Details and links will come in next post.

Monday, September 18, 2017

Day 11

Finished proof of Immerman-Szelepcsényi. Links in last post.

Defined oracle Turing machines and the Baker-Gill-Solovay result that there exist oracles A and B such that
  • PA = NPA
  • PB ≠ NPB
Links:
  • Relativizations of the P =? NP Question by Baker, Gill and Solovay
  • You can simply take A to be any PSPACE-complete problem. For B, here is a good write-up
  • Friday, September 15, 2017

    Day 10

    Today we covered the complements of languages and co-classes and did most of the proof of the Immerman-Szelepcsényi theorem that non-deterministic space is closed under complement. In particular we focused on the proof that NL = co-NL

    Definitions:

    • The complement of a language L, denoted L, is the set of strings not in L.
    • For a complexity class C, we define co-C as the set of languages L for L in C.
    Links:

    Wednesday, September 13, 2017

    Day 9

    We looked at deterministic and nondeterministic space complexity and showed the basic relationships:

    DTIME(f(n)) ⊆ NTIME(f(n)) ⊆ DSPACE(f(n)) ⊆ NSPACE(f(n)) ⊆ ∪c DTIME(cf(n))

    We also proved Savitch's theorem that NSPACE(s(n)) is contained in DSPACE(s2(n)).

    Links:

    Friday, September 8, 2017

    Day 8

    Assignment 1, due Monday, September 18, has been posted on T-Square. I highly recommend you start thinking about the problems immediately.

    The Berman-Hartmanis Isomorphism Conjecture states that all NP-complete sets are polynomial-time isomorphic to each other. The conjecture is still open.

    Let Σn be the set of all strings of length n and Σ≤n be the set of all strings of length at most n.

    We say a set S is dense if for some ε>0, |S∩Σ≤n| ≥ 2nε for all n. A set S is sparse if for some c, |S∩Σn|≤ nc. Every finite set is sparse and no dense set is sparse.

    We showed in class that if the Berman-Hartmanis conjecture is true then every NP-complete set is dense. In paticular under this assumption there are no sparse or finite NP-complete sets and P ≠ NP.

    The Berman-Hartmanis conjecture remains open but Mahaney proved the following: If there is a sparse NP-complete set then P = NP. In class we gave the proof of Ogihara and Watanabe which I also wrote up in my blog.

    Wednesday, September 6, 2017

    Day 7

    The Cantor-Bernstein Theorem states that for any two arbitrary sets A and B if there are 1-1 functions from A to B and B to A then there is a 1-1 and onto function from A to B. How does this carry into complexity?

    Two sets of strings A and B are polynomial-time isomorphic if there is a function f:Σ*→Σ*
    1. f is computable in polynomial time
    2. The inverse of f is computable in polynomial time
    3. f reduces A to B (x in A iff f(x) in B)
    4. f is 1-1
    5. f is onto
    We say f is a length-increasing invertible reduction if there is a f:Σ*→Σ*
    1. f is computable in polynomial time
    2. The inverse of f is computable in polynomial time
    3. f is 1-1
    4. f is length-increasing, i.e., |f(x)| > |x| for all strings x.
    We define A to be paddable if there is a function h:Σ*×Σ*→Σ*
    1. h is computable in polynomial time
    2. For all x,y, x is in A iff h(x,y) is in A
    3. For all y, |h(x,y)| > |y|
    4. For all x, y and y' with y≠y', h(x,y)≠h(x,y')
    5. There is a polynomial-time computable function g s.t. for all x,y, g(h(x,y))=y
    We showed (see Berman-Hartmanis) that
    1. Every A in NP reduces to Satisfiability via a 1-1 length-increasing reduction.
    2. If B reduces to a paddable set A then there is a reduction from B to A that is length-increasing and invertible. 
    3. If A and B reduce to each other via length-increasing invertible reductions then they are isomorphic.
    Essentially all the commonly known NP-complete problems are paddable and thus isomorphic to Satisfiability and each other.

    Friday, September 1, 2017

    Day 6

    We proved Ladner's Theorem that there exists a set A such that A is in NP, A is not in P and A is not NP-complete. Here is a write-up.

    Enjoy the Labor Day weekend and see you on Wednesday.

    Wednesday, August 30, 2017

    Day 5

    Finished proof of Cook's theorem. Talked about potential NP-incomplete problems such as Graph Isomorphism, recently shown in quasipolynomial time by Laszlo Babai.

    Monday, August 28, 2017

    Friday, August 25, 2017

    Day 3

    Reducibility and NP-Completeness

    Papers:



    Wednesday, August 23, 2017

    Day 2

    More introduction to computation, Turing Machines and time complexity. Defined P and briefly NP.

    Papers mentioned:
    Alan Turing, On Computable Numbers, with an Application to the Entscheidungsproblem
    Where he defines the Turing machine. Section 9 is worth reading where he basically defends his thesis that this model defines computability.
    Juris Hartmanis and Richard Stearns, On the Computational Complexity of Algorithms
    This paper started the field of computational complexity. It's major insight was to consider resources like time and memory as a function of the input size.
    Jack Edmonds, Paths, Trees and Flowers
    This paper gives a polynomial-time algorithm for graph matching and argues that polynomial-time is a reasonable stand in for efficient computation.
    Alan Cobham, The Intrinsic Computational Difficulty of Functions
    Cobham gave a similar argument for polynomial time.

    Monday, August 21, 2017

    Day 1

    Guest lecture by Venkat.

    Covered basics of Turing Machines, Complexity Classes and Polynomial Time.

    Reminder that Assignment 0 is due Friday. Download and submit on T-Square.

    Assignment 0

    A short assignment is posted on T-Square and due on Friday.

    Friday, June 9, 2017

    Course Overview

    CS 6520 Computational Complexity
    Fall 2017


    Instructor: Lance Fortnow

    Time: MWF 9:05-9:55
    Room: Whitaker Biomedical Engineering 1103

    Description: This course will give an overview of advanced topics in computational complexity including the P versus NP problem, the structure of NP, the polynomial-time hierarchy, circuit complexity, randomness, counting, interactive and probabilistically checkable proofs.

    Detailed list of topics

    Prerequisites: A strong mathematical background and a basic knowledge of Turing machines and the P v NP problem at the level of CS 4510. This is a course meant for advanced students with a strong interest in theoretical computer science--if you just want to fulfill the graduate theory requirement you should take CS 6505.

    Textbook: None. Computational Complexity by Arora and Barak cover much of the material from the course but we won't be following them.

    Coursework: Assignments roughly every other week. This is a theoretical course and assignments will be problem solving and will not involve any programming. There are no exams.

    Course Website: gtcc17.blogspot.com