The Polynomial Hierarchy

Slides:



Advertisements
Similar presentations
Complexity Theory Lecture 6
Advertisements

The Polynomial – Time Hierarchy
Resource-Bounded Computation
Lecture 16 Deterministic Turing Machine (DTM) Finite Control tape head.
1 Nondeterministic Space is Closed Under Complement Presented by Jing Zhang and Yingbo Wang Theory of Computation II Professor: Geoffrey Smith.
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen.
Alternation Alternation: generalizes non-determinism, where each state is either “existential” or “universal”: Old: existential states New: universal states.
Polynomial-Time Hierarchy 1. Stockmeyer 2. Wrathall.
Complexity 12-1 Complexity Andrei Bulatov Non-Deterministic Space.
Complexity 15-1 Complexity Andrei Bulatov Hierarchy Theorem.
NP-completeness Sipser 7.4 (pages 271 – 283). CS 311 Mount Holyoke College 2 The classes P and NP NP = ∪ k NTIME(n k ) P = ∪ k TIME(n k )
NP-completeness Sipser 7.4 (pages 271 – 283). CS 311 Fall The classes P and NP NP = ∪ k NTIME(n k ) P = ∪ k TIME(n k )
1 Introduction to Computability Theory Lecture11: Variants of Turing Machines Prof. Amos Israeli.
Complexity ©D.Moshkovitz 1 Turing Machines. Complexity ©D.Moshkovitz 2 Motivation Our main goal in this course is to analyze problems and categorize them.
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.
Submitted by : Estrella Eisenberg Yair Kaufman Ohad Lipsky Riva Gonen Shalom.
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.
CS5371 Theory of Computation Lecture 10: Computability Theory I (Turing Machine)
Complexity ©D. Moshkovitz 1 And Randomized Computations The Polynomial Hierarchy.
February 20, 2015CS21 Lecture 191 CS21 Decidability and Tractability Lecture 19 February 20, 2015.
PSPACE  IP Proshanto Mukherji CSC 486 April 23, 2001.
CS5371 Theory of Computation Lecture 12: Computability III (Decidable Languages relating to DFA, NFA, and CFG)
Theory of Computing Lecture 19 MAS 714 Hartmut Klauck.
PSPACE-Completeness Section 8.3 Giorgi Japaridze Theory of Computability.
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 Department of Computer Science University of Texas-Pan American.
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen.
חישוביות וסיבוכיות Computability and Complexity Lecture 7 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A AAAA.
February 18, 2015CS21 Lecture 181 CS21 Decidability and Tractability Lecture 18 February 18, 2015.
Theory of Computing Lecture 17 MAS 714 Hartmut Klauck.
Peter van Emde Boas: Games and Computer Science 1999 GAMES AND COMPUTER SCIENCE Theoretical Models 1999 Peter van Emde Boas References available at:
CSCI 2670 Introduction to Theory of Computing November 29, 2005.
CS151 Complexity Theory Lecture 12 May 6, QSAT is PSPACE-complete Theorem: QSAT is PSPACE-complete. Proof: 8 x 1 9 x 2 8 x 3 … Qx n φ(x 1, x 2,
Relative Universality On classes of Real Computation Hector Zenil University of Lille 1 Université de Paris 1 (Panthéon-Sorbonne)
A Problem That Is Complete for PSPACE (Polynomial Space) BY TEJA SUDHA GARIGANTI.
Alternation Section 10.3 Giorgi Japaridze Theory of Computability.
Complexity ©D.Moshkovits 1 2-Satisfiability NOTE: These slides were created by Muli Safra, from OPICS/sat/)
NP-complete Languages
CSCI 2670 Introduction to Theory of Computing December 2, 2004.
CSCI 2670 Introduction to Theory of Computing December 7, 2005.
Complexity ©D.Moshkovitz 1 Our First NP-Complete Problem The Cook-Levin theorem A B C.
Theory of Computational Complexity Yuji Ishikawa Avis lab. M1.
 2005 SDU Lecture15 P,NP,NP-complete.  2005 SDU 2 The PATH problem PATH = { | G is a directed graph that has a directed path from s to t} s t
Recursively Enumerable and Recursive Languages. Definition: A language is recursively enumerable if some Turing machine accepts it.
Numerical Analysis Lecture 25.
Busch Complexity Lectures: Reductions
Umans Complexity Theory Lectures
Theory of Computability
Turing Machines Acceptors; Enumerators
Jaya Krishna, M.Tech, Assistant Professor
CSC 4170 Theory of Computation Space complexity Chapter 8.
CSCI 2670 Introduction to Theory of Computing
Lecture 10: Query Complexity
Recall last lecture and Nondeterministic TMs
Turing Machines Complexity ©D.Moshkovitz.
CS21 Decidability and Tractability
CS21 Decidability and Tractability
Umans Complexity Theory Lectures
CS21 Decidability and Tractability
Our First NP-Complete Problem
The Polynomial Hierarchy
Reductions Complexity ©D.Moshkovitz.
Reductions Complexity ©D.Moshkovitz.
Theory of Computability
The Polynomial Hierarchy Enumeration Problems 7.3.3
Instructor: Aaron Roth
Instructor: Aaron Roth
Intro to Theory of Computation
Presentation transcript:

The Polynomial Hierarchy

Deciding Satifiability We’ve already seen, that deciding whether a formula is satisfiable… x1 …xn(x1x2x8)… (x6x3) x1x2x3… [(x1x2x8)…(x6x3)] only existential quantifier existential & universal quantifiers PSPACE-complete NP-complete

Technical Note x1x2…xk is the same as x=<x1,x2,…,xk> Thus, allowing several adjacent quantifiers of the same type does not change the problem.

i alternating quantifiers The Hierarchy Definition (i): i is the class of all languages reducible to deciding the sat. of a formula of type: x1x2 x3… R(x1,x2,x3,…) i alternating quantifiers

i alternating quantifiers The Hierarchy Definition (i): i is the class of all languages reducible to deciding the sat. of a formula of type: x1x2x3… R(x1,x2,x3,…) i alternating quantifiers

PH (Polynomial-time Hierarchy) Definition: PH = i i

Simple Observations “base”: 1=NP “connection between  and ”: i=coi “hierarchy”: ii+1 and ii+1 “upper bound”: PHPSPACE

Can the Hierarchy Collapse? Proposition: If NP=coNP, then PH=NP. Proof Idea: By induction on i, i=NP.

Recall: Arithmetical Hierarchy co-r.e. DEC=r.e.co-r.e. DEC=Decidable Sets

Oracle TM Generalize TM M to “oracle TM” M with states Q,Y,N and special “oracle tape.” When M enters state Q it jumps to Y if word on oracle tape is in B otherwise jumps to N. Oracle B Y Q N oracle tape M will erase the oracle tape after entering Y/N

PTIME executions

NP executions (configuration trees)

Alternating Computation Configuration Type Existential Universal Negating + - + Accepting - - + + - + - Rejecting Computation Tree

Back to Alternation For an accepting tree there exists a witness subtree for acceptance (and similar for rejection) Witness subtree contains a single accepting son for every accepting node, and a single rejecting son for every rejecting node A witness subtree is finite, even when the tree itself is infinite! Infinite branches are irrelevant!

Negating Nodes ? Create for every node its dual node which yields the “same” transitions Dual of accepting node is rejecting Dual of rejecting node is accepting Dual of universal node is existential Dual of existential node is universal Dual of Dual is identity Replace every negating node by an existential one, dualizing the entire subtree below it (think de Morgan!) Negating states are unnecessary - by dualizing parts of the computation tree they can be removed.

Eliminating Negating Nodes + + + - - + - - + + + - + + - - + + + - - - + + + - - - + - - + - + - + + + - + - - + + - + + - - - - + + - - Dualized nodes -