Complexity 24-1 Complexity Andrei Bulatov Interactive Proofs.

Slides:



Advertisements
Similar presentations
Based on Powerpoint slides by Giorgi Japaridze, Villanova University Space Complexity and Interactive Proof Systems Sections 8.0, 8.1, 8.2, 8.3, 10.4.
Advertisements

INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011.
Lecture 23. Subset Sum is NPC
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen.
Dana Moshkovitz. Back to NP L  NP iff members have short, efficiently checkable, certificates of membership. Is  satisfiable?  x 1 = truex 11 = true.
Computability and Complexity
Having Proofs for Incorrectness
1 Slides by Dana Moshkovitz. Adapted from Oded Goldreich’s course lecture notes.
Complexity 25-1 Complexity Andrei Bulatov #P-Completeness.
Complexity 11-1 Complexity Andrei Bulatov NP-Completeness.
Complexity 12-1 Complexity Andrei Bulatov Non-Deterministic Space.
Complexity 15-1 Complexity Andrei Bulatov Hierarchy Theorem.
Complexity 11-1 Complexity Andrei Bulatov Space Complexity.
Complexity 26-1 Complexity Andrei Bulatov Interactive Proofs.
Complexity 13-1 Complexity Andrei Bulatov Hierarchy Theorem.
Computability and Complexity 14-1 Computability and Complexity Andrei Bulatov Cook’s Theorem.
Complexity 18-1 Complexity Andrei Bulatov Probabilistic Algorithms.
Computability and Complexity 13-1 Computability and Complexity Andrei Bulatov The Class NP.
Computability and Complexity 19-1 Computability and Complexity Andrei Bulatov Non-Deterministic Space.
CS151 Complexity Theory Lecture 7 April 20, 2004.
1 Adapted from Oded Goldreich’s course lecture notes.
Computability and Complexity 15-1 Computability and Complexity Andrei Bulatov NP-Completeness.
Randomized Computation Roni Parshani Orly Margalit Eran Mantzur Avi Mintz
Zero-Knowledge Proof System Slides by Ouzy Hadad, Yair Gazelle & Gil Ben-Artzi Adapted from Ely Porat course lecture notes.
Complexity 19-1 Complexity Andrei Bulatov More Probabilistic Algorithms.
Computability and Complexity 20-1 Computability and Complexity Andrei Bulatov Class NL.
Analysis of Algorithms CS 477/677
Computability and Complexity 24-1 Computability and Complexity Andrei Bulatov Approximation.
CS151 Complexity Theory Lecture 13 May 11, CS151 Lecture 132 Outline Natural complete problems for PH and PSPACE proof systems interactive proofs.
Zero Knowledge Proofs. Interactive proof An Interactive Proof System for a language L is a two-party game between a verifier and a prover that interact.
CS151 Complexity Theory Lecture 15 May 18, CS151 Lecture 152 Outline IP = PSPACE Arthur-Merlin games –classes MA, AM Optimization, Approximation,
Lecture 20: April 12 Introduction to Randomized Algorithms and the Probabilistic Method.
PSPACE  IP Proshanto Mukherji CSC 486 April 23, 2001.
The Polynomial Hierarchy By Moti Meir And Yitzhak Sapir Based on notes from lectures by Oded Goldreich taken by Ronen Mizrahi, and lectures by Ely Porat.
INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011.
Definition: Let M be a deterministic Turing Machine that halts on all inputs. Space Complexity of M is the function f:N  N, where f(n) is the maximum.
1 The Theory of NP-Completeness 2012/11/6 P: the class of problems which can be solved by a deterministic polynomial algorithm. NP : the class of decision.
Nattee Niparnan. Easy & Hard Problem What is “difficulty” of problem? Difficult for computer scientist to derive algorithm for the problem? Difficult.
Lecture 22 More NPC problems
CS151 Complexity Theory Lecture 13 May 11, Outline proof systems interactive proofs and their power Arthur-Merlin games.
1 Interactive Proofs proof systems interactive proofs and their power Arthur-Merlin games.
CSCI 2670 Introduction to Theory of Computing November 29, 2005.
NP-COMPLETENESS PRESENTED BY TUSHAR KUMAR J. RITESH BAGGA.
Complexity 25-1 Complexity Andrei Bulatov Counting Problems.
EMIS 8373: Integer Programming NP-Complete Problems updated 21 April 2009.
Interactive proof systems Section 10.4 Giorgi Japaridze Theory of Computability.
Fall 2013 CMU CS Computational Complexity Lectures 8-9 Randomness, communication, complexity of unique solutions These slides are mostly a resequencing.
NP-Complete Problems Algorithm : Design & Analysis [23]
CS6045: Advanced Algorithms NP Completeness. NP-Completeness Some problems are intractable: as they grow large, we are unable to solve them in reasonable.
Chapter 11 Introduction to Computational Complexity Copyright © 2011 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1.
COMPLEXITY. Satisfiability(SAT) problem Conjunctive normal form(CNF): Let S be a Boolean expression in CNF. That is, S is the product(and) of several.
NP-complete Languages
CSCI 2670 Introduction to Theory of Computing December 2, 2004.
NP ⊆ PCP(n 3, 1) Theory of Computation. NP ⊆ PCP(n 3,1) What is that? NP ⊆ PCP(n 3,1) What is that?
CSCI 2670 Introduction to Theory of Computing December 7, 2005.
COMPLEXITY. Satisfiability(SAT) problem Conjunctive normal form(CNF): Let S be a Boolean expression in CNF. That is, S is the product(and) of several.
Theory of Computational Complexity Yuji Ishikawa Avis lab. M1.
Zero-Knowledge Proofs Ben Hosp. Classical Proofs A proof is an argument for the truth or correctness of an assertion. A classical proof is an unambiguous.
The NP class. NP-completeness Lecture2. The NP-class The NP class is a class that contains all the problems that can be decided by a Non-Deterministic.
Complexity 27-1 Complexity Andrei Bulatov Interactive Proofs (continued)
P & NP.
Probabilistic Algorithms
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
CSE 589 Applied Algorithms Spring 1999
Instructor: Aaron Roth
Space Complexity and Interactive Proof Systems
Intro to Theory of Computation
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Presentation transcript:

Complexity 24-1 Complexity Andrei Bulatov Interactive Proofs

Complexity 24-2 Interactive Proofs ProverVerifier Has unlimited computational power Can perform polynomial time computations They can exchange messages Wants to convince Verifier in something Accepts or rejects after performing some computation

Complexity 24-3 Formal Model Prover and Verifier are represented by functions: Verifier (V) Input: an input string, w a random string, r message history Output: A message, that is a string, or accept or reject Prover (P) Input: an input string, w message history Output:A message, that is a string

Complexity 24-4 A Prover’s move is the message A Verifier’s move is the message We write if for some k and if for some k We assume that the length of messages, the length of r, and k are bounded with a polynomial p(|w|) Finally, we denote where r is a random string of length p(|w|)

Complexity 24-5 The Class IP Definition A language L belongs to IP if some polynomial time function V and arbitrary function P exist, such that for every function R and string w w  L implies Pr[V  P accepts w]  w  L implies Pr[V  R accepts w] 

Complexity 24-6 Examples Instance: Graphs G and H. Question: Are G and H non-isomorphic? Graph Non-Isomorphism Instance: A formula  in CNF and a number k Question: Is it true that  has exactly k satisfying assignments? #SAT Theorem #SAT  IP

Complexity 24-7 Proof Given a formula  and a number k. Let be the variables of  The first stage is “arithmetization” of  : We construct a polynomial such that

Complexity 24-8 By induction for X define the corresponding polynomial to be x for  X define the corresponding polynomial to be 1 – x for    with corresponding polynomials g and h, define the corresponding polynomial to be gh (the product) Arithmetization for    with corresponding polynomials g and h, define the corresponding polynomial to be 1 – (1 – g)(1 – h) for  with corresponding polynomial g, define the corresponding polynomial to be 1 – g

Complexity 24-9 Protocol Let be the result of arithmetization of  Then The protocol will go in n rounds On every round, we will construct from an equality an equality of the form

Complexity The property we are trying to achieve is if and only if If this is possible, starting off with after n rounds we get an equality which is easy to verify The last equality is true if and only if the first one is true

If was true before this round, then Complexity Randomness Unfortunately, the condition above is impossible to achieve Instead, we use a probabilistic one is true after this round with probability 1 If was false before the this round, then is false after this round with probability 1– 

Complexity Thus, after n rounds If we started off with a correct equality, then after n rounds we have a correct equality with probability 1 If we started off with an incorrect equality, then after n rounds the probability that we end up with a correct equality is the probability that the error was made in one of the n rounds, which is at most n . Thus, with probability at least 1 – n , we will have an incorrect equality We get an equality of the form that can be easily checked We need to organize every round such that

Complexity Round Having the equality Verifier sends to Prover the polynomial g Prover returns the polynomial or what he pretends this polynomial is Verifier checks if the degree of h is not higher than that of g Verifier checks if Verifier randomly chooses a number r, such that where m is the number of clauses in , and replace the original equality with

Complexity Analysis If the original equality is true then Prover should behave honestly, that is return actual coefficients of h(x). Then - The degree of h is not higher than that of g - h(0) + h(1) = k - For any r,

Complexity If the original equation is not true then consider two cases - if Prover is honest that is he returns the actual coefficients of h, then h(0) + h(1)  k - if Prover is dishonest that is he returns some polynomial p(x) such that the degree of p is not higher than that of g and p(0) + p(1) = k In this case g and h are different. Therefore their values can be equal for at most d = max{deg(g), deg(h)} values of x. The probability that for a randomly chosen is

Complexity Note that the degree of f is at most mn. Therefore

Complexity IP = PSPACE Theorem IP = PSPACE Proof. IP  PSPACE If we consider Prover’s messages as nondeterministic guesses, then we get IP  NPSPACE Then, by Savitch’s theorem IP  NPSPACE = PSPACE

Complexity PSPACE  IP It is sufficient to show that some PSPACE-complete problem belongs to IP Instance: A quantified Boolean formula where each is a Boolean variable, is a Boolean expression involving and each is a quantifier (  or  ). Question: Is  logically valid ? Quantified Boolean Formula

Complexity Arithmetization Given a formula Let be the arithmetization of  Then define polynomials by setting Clearly,  is true if and only if

Complexity Reducing degree Since the degree of may be exponential, we need to reduce it. Replace  with or where and We define as follows:

Complexity Properties of the new polynomials If then when is linear in x Therefore if then is a linear polynomial

Complexity Protocol Step 0. P  V: Prover sends to Verifier Verifier checks if and reject if not Step i. P  V: Prover sends as a polynomial in z. Here denotes the previously selected random values for variables Verifier computes and. Then it checks the degree of the polynomial and that or If either fails, Verifier rejects V  P: Verifier picks a random value and sends it to Prover

Complexity Step k + 1. Verifier checks if If yes then Verifier accept, if not rejects