Complexity 27-1 Complexity Andrei Bulatov Interactive Proofs (continued)

Slides:



Advertisements
Similar presentations
Quantum Information and the PCP Theorem Ran Raz Weizmann Institute.
Advertisements

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.
Complexity Theory Lecture 3 Lecturer: Moni Naor. Recap Last week: Non deterministic communication complexity Probabilistic communication complexity Their.
IP=PSPACE Nikhil Srivastava CPSC 468/568. Outline IP Warmup: coNP  IP by arithmetization PSPACE (wrong) attempt at PSPACE  IP (revised) PSPACE  IP.
INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011.
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
Alternation Alternation: generalizes non-determinism, where each state is either “existential” or “universal”: Old: existential states New: universal states.
1 Slides by Dana Moshkovitz. Adapted from Oded Goldreich’s course lecture notes.
Complexity 25-1 Complexity Andrei Bulatov #P-Completeness.
Computability and Complexity 20-1 Computability and Complexity Andrei Bulatov Random Sources.
Peter van Emde Boas: Playing Savage SOFSEM 2000 Milovy PLAYING SAVITCH Peter van Emde Boas ILLC-FNWI-Univ. of Amsterdam Bronstee.com Software and Services.
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.
Computability and Complexity 9-1 Computability and Complexity Andrei Bulatov Logic Reminder (Cnt’d)
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.
P and NP Sipser (pages ). CS 311 Fall Polynomial time P = ∪ k TIME(n k ) … P = ∪ k TIME(n k ) … TIME(n 3 ) TIME(n 2 ) TIME(n)
Computability and Complexity 19-1 Computability and Complexity Andrei Bulatov Non-Deterministic Space.
1 Adapted from Oded Goldreich’s course lecture notes.
Computability and Complexity 15-1 Computability and Complexity Andrei Bulatov NP-Completeness.
Complexity 19-1 Complexity Andrei Bulatov More Probabilistic Algorithms.
Computability and Complexity 20-1 Computability and Complexity Andrei Bulatov Class NL.
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.
CS151 Complexity Theory Lecture 15 May 18, CS151 Lecture 152 Outline IP = PSPACE Arthur-Merlin games –classes MA, AM Optimization, Approximation,
Fall 2004COMP 3351 Time Complexity We use a multitape Turing machine We count the number of steps until a string is accepted We use the O(k) notation.
Computability and Complexity 10-1 Computability and Complexity Andrei Bulatov Gödel’s Incompleteness Theorem.
PSPACE  IP Proshanto Mukherji CSC 486 April 23, 2001.
Computability and Complexity 17-1 Computability and Complexity Andrei Bulatov Strong NP-Completeness.
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.
The Complexity of Primality Testing. What is Primality Testing? Testing whether an integer is prime or not. – An integer p is prime if the only integers.
Lecture 22 More NPC problems
Space Complexity. Reminder: P, NP classes P NP is the class of problems for which: –Guessing phase: A polynomial time algorithm generates a plausible.
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.
Complexity 25-1 Complexity Andrei Bulatov Counting Problems.
Interactive proof systems Section 10.4 Giorgi Japaridze Theory of Computability.
A Problem That Is Complete for PSPACE (Polynomial Space) BY TEJA SUDHA GARIGANTI.
Probabilistic verification Mario Szegedy, Rutgers www/cs.rutgers.edu/~szegedy/07540 Lecture 1.
1 Time Complexity We use a multitape Turing machine We count the number of steps until a string is accepted We use the O(k) notation.
Complexity 24-1 Complexity Andrei Bulatov Interactive Proofs.
Homework 8 Solutions Problem 1. Draw a diagram showing the various classes of languages that we have discussed and alluded to in terms of which class.
NP ⊆ PCP(n 3, 1) Theory of Computation. NP ⊆ PCP(n 3,1) What is that? NP ⊆ PCP(n 3,1) What is that?
Prof. Busch - LSU1 Time Complexity. Prof. Busch - LSU2 Consider a deterministic Turing Machine which decides a language.
Theory of Computational Complexity Yuji Ishikawa Avis lab. M1.
Complexity of Compositional Model Checking of Computation Tree Logic on Simple Structures Krishnendu Chatterjee Pallab Dasgupta P.P. Chakrabarti IWDC 2004,
Probabilistic Algorithms
On the Size of Pairing-based Non-interactive Arguments
Lecture 2-2 NP Class.
Computability and Complexity
Time Complexity Costas Busch - LSU.
Complexity 6-1 The Class P Complexity Andrei Bulatov.
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Time Complexity We use a multitape Turing machine
NP-Complete Problems.
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Space Complexity and Interactive Proof Systems
The Satisfiability Problem
Intro to Theory of Computation
Lecture 23 NP-Hard Problems
Interactive Proofs Adapted from Oded Goldreich’s course lecture notes.
Presentation transcript:

Complexity 27-1 Complexity Andrei Bulatov Interactive Proofs (continued)

Complexity 27-2 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 27-3 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 27-4 Arithmetization Given a formula Let be the arithmetization of  Then define polynomials by setting Clearly,  is true if and only if

Complexity 24-5 Reducing degree Since the degree of may be exponential, we need to reduce it. Replace  with or where and We define as follows:

Complexity 27-6 Properties of the new polynomials If then when is linear in x Therefore if then is a linear polynomial

Complexity 27-7 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 27-8 Step k + 1. Verifier checks if If yes then Verifier accept, if not rejects