Polynomial-Time Hierarchy 1. Stockmeyer 2. Wrathall.

Slides:



Advertisements
Similar presentations
Completeness and Expressiveness
Advertisements

Some important properties Lectures of Prof. Doron Peled, Bar Ilan University.
Function Technique Eduardo Pinheiro Paul Ilardi Athanasios E. Papathanasiou The.
Complexity Theory Lecture 6
Resource-Bounded Computation
Isolation Technique April 16, 2001 Jason Ku Tao Li.
1 Savitch and Immerman- Szelepcsènyi Theorems. 2 Space Compression  For every k-tape S(n) space bounded offline (with a separate read-only input tape)
 2004 SDU Lecture17-P,NP, NPC.  2004 SDU 2 1.Decision problem and language decision problem decision problem and language 2.P and NP Definitions of.
CS151 Complexity Theory Lecture 3 April 6, CS151 Lecture 32 Introduction A motivating question: Can computers replace mathematicians? L = { (x,
Department of Computer Science & Engineering
Chapter 5 The Witness Reduction Technique: Feasible Closure Properties of #P Greg Goldstein Andrew Learn 18 April 2001.
Complexity class NP Is the class of languages that can be verified by a polynomial-time algorithm. L = { x in {0,1}* | there exists a certificate y with.
Having Proofs for Incorrectness
Complexity 15-1 Complexity Andrei Bulatov Hierarchy Theorem.
Complexity 13-1 Complexity Andrei Bulatov Hierarchy Theorem.
CS151 Complexity Theory Lecture 3 April 6, Nondeterminism: introduction A motivating question: Can computers replace mathematicians? L = { (x,
P, NP, PS, and NPS By Muhannad Harrim. Class P P is the complexity class containing decision problems which can be solved by a Deterministic Turing machine.
The Counting Class #P Slides by Vera Asodi & Tomer Naveh
FORMAL LANGUAGES, AUTOMATA AND COMPUTABILITY Read sections 7.1 – 7.3 of the book for next time.
Complexity ©D.Moshkovits 1 Space Complexity Complexity ©D.Moshkovits 2 Motivation Complexity classes correspond to bounds on resources One such resource.
Deciding Primality is in P M. Agrawal, N. Kayal, N. Saxena Slides by Adi Akavia.
Arithmetic Hardness vs. Randomness Valentine Kabanets SFU.
CS151 Complexity Theory Lecture 12 May 6, CS151 Lecture 122 Outline The Polynomial-Time Hierarachy (PH) Complete problems for classes in PH, PSPACE.
1 Slides by Golan Weisz, Omer Ben Shalom Nir Ailon & Tal Moran Adapted from Oded Goldreich’s course lecture notes by Moshe Lewenstien, Yehuda Lindell.
Complexity1 Pratt’s Theorem Proved. Complexity2 Introduction So far, we’ve reduced proving PRIMES  NP to proving a number theory claim. This is our next.
Chapter 1 The Self-Reducibility Technique Matt Boutell and Bill Scherer CSC 486 April 4, 2001.
Submitted by : Estrella Eisenberg Yair Kaufman Ohad Lipsky Riva Gonen Shalom.
Regular Expression (EXTRA)
Computability and Complexity 20-1 Computability and Complexity Andrei Bulatov Class NL.
Alternating Turing Machine (ATM) –  node is marked accept iff any of its children is marked accept. –  node is marked accept iff all of its children.
Complexity ©D. Moshkovitz 1 And Randomized Computations The Polynomial Hierarchy.
Complements of Languages in NP Osama Awwad Department of Computer Science Western Michigan University July 13, 2015.
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.
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.
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen.
Lecture 22 More NPC problems
Theory of Computing Lecture 17 MAS 714 Hartmut Klauck.
NP Complexity By Mussie Araya. What is NP Complexity? Formal Definition: NP is the set of decision problems solvable in polynomial time by a non- deterministic.
Prabhas Chongstitvatana1 NP-complete proofs The circuit satisfiability proof of NP- completeness relies on a direct proof that L  p CIRCUIT-SAT for every.
Space Complexity. Reminder: P, NP classes P NP is the class of problems for which: –Guessing phase: A polynomial time algorithm generates a plausible.
CSCI 2670 Introduction to Theory of Computing November 29, 2005.
1 2 Probabilistic Computations  Extend the notion of “efficient computation” beyond polynomial-time- Turing machines.  We will still consider only.
Number Theory Project The Interpretation of the definition Andre (JianYou) Wang Joint with JingYi Xue.
1 Design and Analysis of Algorithms Yoram Moses Lecture 11 June 3, 2010
1. 2 Lecture outline Basic definitions: Basic definitions: P, NP complexity classes P, NP complexity classes the notion of a certificate. the notion of.
P Vs NP Turing Machine. Definitions - Turing Machine Turing Machine M has a tape of squares Each Square is capable of storing a symbol from set Γ (including.
NP-Completness Turing Machine. Hard problems There are many many important problems for which no polynomial algorithms is known. We show that a polynomial-time.
Complexity 24-1 Complexity Andrei Bulatov Interactive Proofs.
NP-complete Languages
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.
Space Complexity. Reminder: P, NP classes P is the class of problems that can be solved with algorithms that runs in polynomial time NP is the class of.
CSCI 2670 Introduction to Theory of Computing December 2, 2004.
1 Discrete Mathematical Mathematical Induction ( الاستقراء الرياضي )
1 Introduction to Quantum Information Processing QIC 710 / CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lectures
Theory of Computational Complexity Yuji Ishikawa Avis lab. M1.
1 Design and Analysis of Algorithms Yoram Moses Lecture 13 June 17, 2010
Chapter 5 Recursive and recursively enumerable functions Phrase-structure grammars.
Complexity 27-1 Complexity Andrei Bulatov Interactive Proofs (continued)
NP-Completeness A problem is NP-complete if: It is in NP
Computational Complexity Theory
Umans Complexity Theory Lectures
Theory of Computability
Lecture 24 NP-Complete Problems
CS151 Complexity Theory Lecture 3 April 10, 2017.
Finite Model Theory Lecture 6
Prabhas Chongstitvatana
Umans Complexity Theory Lectures
Our First NP-Complete Problem
The Polynomial Hierarchy
The Polynomial Hierarchy
Presentation transcript:

Polynomial-Time Hierarchy 1. Stockmeyer 2. Wrathall

Definitions Let A Θ + and B Δ + for finite alphabets Θ and Δ. A transforms to B within logspace via f (A B via f) iff f is a transformation, f:Θ + → Δ +, such that f є logspace and xєA ↔ f(x)єB for all x є Θ +

The Hierarchy  The polynomial time hierarchy is where: and for k≥0 Also define

2 notes  Note that and. Since obviously BєP B and for any set B, the P-hierarchy has the following structure:  Also

Lemmas  Let L a language and i≥1. L in Σ k P iff there is a poly-balanced relation R s.t. the language{x,y: (x,y)єR} is in Π k-1 P and L={x: Эy s.t. (x,y) єR}  Let L a language and i≥1. L in Π k P iff there is a poly-balanced relation R s.t. the language{x,y: (x,y)єR} is in Σ k-1 P and L={x: for all y with |y|≤|x| k, (x,y) єR}

Proof  Π k P =co Σ k P so it suffices to prove it for Σ k P.  For i=1 it holds.  Let i>1 and R exists.  NDTM M choses a y nondet. And with a Σ i-1 P oracle decides if (x,y) not in R (since R in Π i-1 P )

Proof continues  Let L in Σ k P we will show that a proper R exists.  L is decided by NDTM M with oracle for Kє Σ i-1 P.  By induction Э relation S s.t. z єK iff Эw : (z,w)єS, Sє Π i-2 P.  R poly-balanced and poly decidable for L. x єL iff Э acc. comput. of M K on x. y records computation of M K.  Some steps are queries to K.  For each yes query (z i ) y will contain the certificate w i s.t. (z i,w i )єS. (x,y)єR iff y records an acc computation of M with a certificate w i for each yes querry z i in computation.\  (x,y)єR can be checked in Π i-1 P

Main Theorem Let L S + be a language. For any k≥1, Lє if and only if there exist polynomials p 1,…,p k and a language L’ є P such that for all x є S +, x є L iff Dually, L є if and only if x є L iff for some L’ є P and polynomials p 1,…,p k

2 propositions 1.For any k ≥ 1, a language L S + is in iff there exist a homomorphism h:S* → T*, a language L’ T + in and a polynomial p(n) such that L=h(L’) and for any x є L’, |x|≤p(|h(x)|), That is ={h(L’): L’ є, h a homomorphism that performs poly-bounded erasing on L’} 2.For each k ≥ 1, is closed under poly-bounded existential quantification and is closed under poly- time bounded universal quantification.

If for some k≥1 then for all j≥K  Assume for some k≥1  By induction on j we will prove it  For j=k it stands  Assume that for some j>k we will show that  From previous theorem: There is a 2-ary relation R and a polynomial p such that for all x, xєA iff  By induction we have R. A because for k≥1, is closed under the operation of poly-bounded existential quantification over variables of relations (prop 2).  Thus and by definition

1.If for some k ≥ 1, then P ≠ NP 2.If contains infinitely main distinct classes, then for all k ≥ 0.  Baker points out that NP PSPACE =PSPACE is an immediate consequence from Savitch’s theorem NSPACE(S(n)) DSPACE(S 2 (N)). By induction on k we have for all k.

If for all k, then  Let k≥1 B k ={F(X 1,…,X k )|F(X 1,…,X k ) is a boolean formula, and }  1.B ω is log-complete in PSPACE. 2.Suppose A B and B є NP C. Then also A є NP C. PH PSPACE. If PSPACE PH then for some j, Since is closed under logspace reductions, implies that and then.