PSPACE-Complete Languages
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CS601 DTIME and DSPACE Lecture 5 Time and Space Functions: T, S
CS601 DTIME and DSPACE Lecture 5 Time and Space functions: t, s : N → N+ Definition 5.1 A set A ⊆ U is in DTIME[t(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M) ≡ w ∈ U M(w)=1 , and 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps. Definition 5.2 A set A ⊆ U is in DSPACE[s(n)] iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. A = L(M), and 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells. (Input tape is “read-only” and not counted as space used.) Example: PALINDROMES ∈ DTIME[n], DSPACE[n]. In fact, PALINDROMES ∈ DSPACE[log n]. [Exercise] 1 CS601 F(DTIME) and F(DSPACE) Lecture 5 Definition 5.3 f : U → U is in F (DTIME[t(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) halts within c · t(|w|) steps; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. Definition 5.4 f : U → U is in F (DSPACE[s(n)]) iff there exists a deterministic, multi-tape TM, M, and a constant c, such that, 1. f = M(·); 2. ∀w ∈ U, M(w) uses at most c · s(|w|) work-tape cells; 3. |f(w)|≤|w|O(1), i.e., f is polynomially bounded. (Input tape is “read-only”; Output tape is “write-only”. -
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs*
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs* Benny Applebaum† Eyal Golombek* Abstract We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, CV , that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communica- tion from the prover by R−F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2R−F . Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero- knowledge holds against a “semi-malicious” verifier that maliciously selects its random tape and then plays honestly. -
EXPSPACE-Hardness of Behavioural Equivalences of Succinct One
EXPSPACE-hardness of behavioural equivalences of succinct one-counter nets Petr Janˇcar1 Petr Osiˇcka1 Zdenˇek Sawa2 1Dept of Comp. Sci., Faculty of Science, Palack´yUniv. Olomouc, Czech Rep. [email protected], [email protected] 2Dept of Comp. Sci., FEI, Techn. Univ. Ostrava, Czech Rep. [email protected] Abstract We note that the remarkable EXPSPACE-hardness result in [G¨oller, Haase, Ouaknine, Worrell, ICALP 2010] ([GHOW10] for short) allows us to answer an open complexity ques- tion for simulation preorder of succinct one counter nets (i.e., one counter automata with no zero tests where counter increments and decrements are integers written in binary). This problem, as well as bisimulation equivalence, turn out to be EXPSPACE-complete. The technique of [GHOW10] was referred to by Hunter [RP 2015] for deriving EXPSPACE-hardness of reachability games on succinct one-counter nets. We first give a direct self-contained EXPSPACE-hardness proof for such reachability games (by adjust- ing a known PSPACE-hardness proof for emptiness of alternating finite automata with one-letter alphabet); then we reduce reachability games to (bi)simulation games by using a standard “defender-choice” technique. 1 Introduction arXiv:1801.01073v1 [cs.LO] 3 Jan 2018 We concentrate on our contribution, without giving a broader overview of the area here. A remarkable result by G¨oller, Haase, Ouaknine, Worrell [2] shows that model checking a fixed CTL formula on succinct one-counter automata (where counter increments and decre- ments are integers written in binary) is EXPSPACE-hard. Their proof is interesting and nontrivial, and uses two involved results from complexity theory. -
Lecture 10: Space Complexity III
Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Lecture 10: Space Complexity III Arijit Bishnu 27.03.2010 Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Outline 1 Space Complexity Classes: NL and L 2 Reductions 3 NL-completeness 4 The Relation between NL and coNL 5 A Relation Among the Complexity Classes Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Outline 1 Space Complexity Classes: NL and L 2 Reductions 3 NL-completeness 4 The Relation between NL and coNL 5 A Relation Among the Complexity Classes Definition for Recapitulation S c NPSPACE = c>0 NSPACE(n ). The class NPSPACE is an analog of the class NP. Definition L = SPACE(log n). Definition NL = NSPACE(log n). Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Space Complexity Classes Definition for Recapitulation S c PSPACE = c>0 SPACE(n ). The class PSPACE is an analog of the class P. Definition L = SPACE(log n). Definition NL = NSPACE(log n). Space Complexity Classes: NL and L Reductions NL-completeness The Relation between NL and coNL A Relation Among the Complexity Classes Space Complexity Classes Definition for Recapitulation S c PSPACE = c>0 SPACE(n ). The class PSPACE is an analog of the class P. Definition for Recapitulation S c NPSPACE = c>0 NSPACE(n ). -
Probabilistic Proof Systems: a Primer
Probabilistic Proof Systems: A Primer Oded Goldreich Department of Computer Science and Applied Mathematics Weizmann Institute of Science, Rehovot, Israel. June 30, 2008 Contents Preface 1 Conventions and Organization 3 1 Interactive Proof Systems 4 1.1 Motivation and Perspective ::::::::::::::::::::::: 4 1.1.1 A static object versus an interactive process :::::::::: 5 1.1.2 Prover and Veri¯er :::::::::::::::::::::::: 6 1.1.3 Completeness and Soundness :::::::::::::::::: 6 1.2 De¯nition ::::::::::::::::::::::::::::::::: 7 1.3 The Power of Interactive Proofs ::::::::::::::::::::: 9 1.3.1 A simple example :::::::::::::::::::::::: 9 1.3.2 The full power of interactive proofs ::::::::::::::: 11 1.4 Variants and ¯ner structure: an overview ::::::::::::::: 16 1.4.1 Arthur-Merlin games a.k.a public-coin proof systems ::::: 16 1.4.2 Interactive proof systems with two-sided error ::::::::: 16 1.4.3 A hierarchy of interactive proof systems :::::::::::: 17 1.4.4 Something completely di®erent ::::::::::::::::: 18 1.5 On computationally bounded provers: an overview :::::::::: 18 1.5.1 How powerful should the prover be? :::::::::::::: 19 1.5.2 Computational Soundness :::::::::::::::::::: 20 2 Zero-Knowledge Proof Systems 22 2.1 De¯nitional Issues :::::::::::::::::::::::::::: 23 2.1.1 A wider perspective: the simulation paradigm ::::::::: 23 2.1.2 The basic de¯nitions ::::::::::::::::::::::: 24 2.2 The Power of Zero-Knowledge :::::::::::::::::::::: 26 2.2.1 A simple example :::::::::::::::::::::::: 26 2.2.2 The full power of zero-knowledge proofs :::::::::::: -
Lecture 4: Space Complexity II: NL=Conl, Savitch's Theorem
COM S 6810 Theory of Computing January 29, 2009 Lecture 4: Space Complexity II Instructor: Rafael Pass Scribe: Shuang Zhao 1 Recall from last lecture Definition 1 (SPACE, NSPACE) SPACE(S(n)) := Languages decidable by some TM using S(n) space; NSPACE(S(n)) := Languages decidable by some NTM using S(n) space. Definition 2 (PSPACE, NPSPACE) [ [ PSPACE := SPACE(nc), NPSPACE := NSPACE(nc). c>1 c>1 Definition 3 (L, NL) L := SPACE(log n), NL := NSPACE(log n). Theorem 1 SPACE(S(n)) ⊆ NSPACE(S(n)) ⊆ DTIME 2 O(S(n)) . This immediately gives that N L ⊆ P. Theorem 2 STCONN is NL-complete. 2 Today’s lecture Theorem 3 N L ⊆ SPACE(log2 n), namely STCONN can be solved in O(log2 n) space. Proof. Recall the STCONN problem: given digraph (V, E) and s, t ∈ V , determine if there exists a path from s to t. 4-1 Define boolean function Reach(u, v, k) with u, v ∈ V and k ∈ Z+ as follows: if there exists a path from u to v with length smaller than or equal to k, then Reach(u, v, k) = 1; otherwise Reach(u, v, k) = 0. It is easy to verify that there exists a path from s to t iff Reach(s, t, |V |) = 1. Next we show that Reach(s, t, |V |) can be recursively computed in O(log2 n) space. For all u, v ∈ V and k ∈ Z+: • k = 1: Reach(u, v, k) = 1 iff (u, v) ∈ E; • k > 1: Reach(u, v, k) = 1 iff there exists w ∈ V such that Reach(u, w, dk/2e) = 1 and Reach(w, v, bk/2c) = 1. -
Supercuspidal Part of the Mod L Cohomology of GU(1,N - 1)-Shimura Varieties
Supercuspidal part of the mod l cohomology of GU(1,n - 1)-Shimura varieties The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Shin, Sug Woo. “Supercuspidal Part of the Mod L Cohomology of GU(1,n - 1)-Shimura Varieties.” Journal für die reine und angewandte Mathematik (Crelles Journal) 2015.705 (2015): 1–21. © 2015 De Gruyter As Published http://dx.doi.org/10.1515/crelle-2013-0057 Publisher Walter de Gruyter Version Final published version Citable link http://hdl.handle.net/1721.1/108760 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. J. reine angew. Math. 705 (2015), 1–21 Journal für die reine und angewandte Mathematik DOI 10.1515/crelle-2013-0057 © De Gruyter 2015 Supercuspidal part of the mod l cohomology of GU.1; n 1/-Shimura varieties By Sug Woo Shin at Cambridge, MA Abstract. Let l be a prime. In this paper we are concerned with GU.1; n 1/-type Shimura varieties with arbitrary level structure at l and investigate the part of the cohomology on which G.Qp/ acts through mod l supercuspidal representations, where p l is any prime ¤ such that G.Qp/ is a general linear group. The main theorem establishes the mod l analogue of the local-global compatibility. Our theorem also encodes a global mod l Jacquet–Langlands correspondence in that the cohomology is described in terms of mod l automorphic forms on some compact inner form of G. -
26 Space Complexity
CS:4330 Theory of Computation Spring 2018 Computability Theory Space Complexity Haniel Barbosa Readings for this lecture Chapter 8 of [Sipser 1996], 3rd edition. Sections 8.1, 8.2, and 8.3. Space Complexity B We now consider the complexity of computational problems in terms of the amount of space, or memory, they require B Time and space are two of the most important considerations when we seek practical solutions to most problems B Space complexity shares many of the features of time complexity B It serves a further way of classifying problems according to their computational difficulty 1 / 22 Space Complexity Definition Let M be a deterministic Turing machine, DTM, that halts on all inputs. The space complexity of M is the function f : N ! N, where f (n) is the maximum number of tape cells that M scans on any input of length n. Definition If M is a nondeterministic Turing machine, NTM, wherein all branches of its computation halt on all inputs, we define the space complexity of M, f (n), to be the maximum number of tape cells that M scans on any branch of its computation for any input of length n. 2 / 22 Estimation of space complexity Let f : N ! N be a function. The space complexity classes, SPACE(f (n)) and NSPACE(f (n)), are defined by: B SPACE(f (n)) = fL j L is a language decided by an O(f (n)) space DTMg B NSPACE(f (n)) = fL j L is a language decided by an O(f (n)) space NTMg 3 / 22 Example SAT can be solved with the linear space algorithm M1: M1 =“On input h'i, where ' is a Boolean formula: 1. -
Computational Complexity
Computational complexity Plan Complexity of a computation. Complexity classes DTIME(T (n)). Relations between complexity classes. Complete problems. Domino problems. NP-completeness. Complete problems for other classes Alternating machines. – p.1/17 Complexity of a computation A machine M is T (n) time bounded if for every n and word w of size n, every computation of M has length at most T (n). A machine M is S(n) space bounded if for every n and word w of size n, every computation of M uses at most S(n) cells of the working tape. Fact: If M is time or space bounded then L(M) is recursive. If L is recursive then there is a time and space bounded machine recognizing L. DTIME(T (n)) = fL(M) : M is det. and T (n) time boundedg NTIME(T (n)) = fL(M) : M is T (n) time boundedg DSPACE(S(n)) = fL(M) : M is det. and S(n) space boundedg NSPACE(S(n)) = fL(M) : M is S(n) space boundedg . – p.2/17 Hierarchy theorems A function S(n) is space constructible iff there is S(n)-bounded machine that given w writes aS(jwj) on the tape and stops. A function T (n) is time constructible iff there is a machine that on a word of size n makes exactly T (n) steps. Thm: Let S2(n) be a space-constructible function and let S1(n) ≥ log(n). If S1(n) lim infn!1 = 0 S2(n) then DSPACE(S2(n)) − DSPACE(S1(n)) is nonempty. Thm: Let T2(n) be a time-constructible function and let T1(n) log(T1(n)) lim infn!1 = 0 T2(n) then DTIME(T2(n)) − DTIME(T1(n)) is nonempty. -
Complexity Theory Lectures 1–6
Complexity Theory 1 Complexity Theory Lectures 1–6 Lecturer: Dr. Timothy G. Griffin Slides by Anuj Dawar Computer Laboratory University of Cambridge Easter Term 2009 http://www.cl.cam.ac.uk/teaching/0809/Complexity/ Cambridge Easter 2009 Complexity Theory 2 Texts The main texts for the course are: Computational Complexity. Christos H. Papadimitriou. Introduction to the Theory of Computation. Michael Sipser. Other useful references include: Computers and Intractability: A guide to the theory of NP-completeness. Michael R. Garey and David S. Johnson. Structural Complexity. Vols I and II. J.L. Balc´azar, J. D´ıaz and J. Gabarr´o. Computability and Complexity from a Programming Perspective. Neil Jones. Cambridge Easter 2009 Complexity Theory 3 Outline A rough lecture-by-lecture guide, with relevant sections from the text by Papadimitriou (or Sipser, where marked with an S). Algorithms and problems. 1.1–1.3. • Time and space. 2.1–2.5, 2.7. • Time Complexity classes. 7.1, S7.2. • Nondeterminism. 2.7, 9.1, S7.3. • NP-completeness. 8.1–8.2, 9.2. • Graph-theoretic problems. 9.3 • Cambridge Easter 2009 Complexity Theory 4 Outline - contd. Sets, numbers and scheduling. 9.4 • coNP. 10.1–10.2. • Cryptographic complexity. 12.1–12.2. • Space Complexity 7.1, 7.3, S8.1. • Hierarchy 7.2, S9.1. • Descriptive Complexity 5.6, 5.7. • Cambridge Easter 2009 Complexity Theory 5 Complexity Theory Complexity Theory seeks to understand what makes certain problems algorithmically difficult to solve. In Data Structures and Algorithms, we saw how to measure the complexity of specific algorithms, by asymptotic measures of number of steps. -
(Design And) Analysis of Algorithms NP and Complexity Classes
Intro P and NP Hard problems CSE 548: (Design and) Analysis of Algorithms NP and Complexity Classes R. Sekar 1 / 38 Intro P and NP Hard problems Search and Optimization Problems Many problems of our interest are search problems with exponentially (or even infinitely) many solutions Shortest of the paths between two vertices Spanning tree with minimal cost Combination of variable values that minimize an objective We should be surprised we find ecient (i.e., polynomial-time) solutions to these problems It seems like these should be the exceptions rather than the norm! What do we do when we hit upon other search problems? 2 / 38 Intro P and NP Hard problems Hard Problems: Where you find yourself ... I can’t find an ecient algorithm, I guess I’m just too dumb. Images from “Computers and Intractability” by Garey and Johnson 3 / 38 Intro P and NP Hard problems Search and Optimization Problems What do we do when we hit upon hard search problems? Can we prove they can’t be solved eciently? 4 / 38 Intro P and NP Hard problems Hard Problems: Where you would like to be ... I can’t find an ecient algorithm, because no such algorithm is possible. Images from “Computers and Intractability” by Garey and Johnson 5 / 38 Intro P and NP Hard problems Search and Optimization Problems Unfortunately, it is very hard to prove that ecient algorithms are impossible Second best alternative: Show that the problem is as hard as many other problems that have been worked on by a host of brilliant scientists over a very long time Much of complexity theory is concerned with categorizing hard problems into such equivalence classes 6 / 38 Intro P and NP Hard problems P, NP, Co-NP, NP-hard and NP-complete 7 / 38 Intro P and NP Hard problems Nondeterminism and Search Problems Nondeterminism is an oft-used abstraction in language theory Non-deterministic FSA Non-deterministic PDA So, why not non-deterministic Turing machines? Acceptance criteria is analogous to NFA and NPDA if there is a sequence of transitions to an accepting state, an NDTM will take that path. -
An Entropy Proof of the Switching Lemma and Tight Bounds on the Decision-Tree Size of AC0
An entropy proof of the switching lemma and tight bounds on the decision-tree size of AC0 Benjamin Rossman∗ University of Toronto November 3, 2017 Abstract We first give a simple entropy argument showing that every m-clause DNF with expected value λ 2 [0; 1] under the uniform distribution has average sensitivity (a.k.a. total influence) at most 2λ log(m/λ). Using a similar idea, we then show the following switching lemma for an m-clause DNF (or CNF) formula F : P t (1) [ DTdepth(F Rp) ≥ t ] ≤ O(p log(m + 1)) : for all p 2 [0; 1] and t 2 N where Rp is the p-random restriction and DTdepth(·) denotes decision- tree depth. Our proof replaces the counting arguments in previous proofs of H˚astad's O(pw)t switching lemma for width-w DNFs [5,9,2] with an entropy argument that naturally applies to unbounded-width DNFs with a bounded number of clauses. With respect to AC0 circuits, our m-clause switching lemma has similar applications as H˚astad'swidth-w switching lemma, 1=(d−1) including a 2Ω(n ) lower bound for PARITY. An additional result of this paper extends inequality (1) to AC0 circuits via a combination of H˚astad'sswitching and multi-switching lemmas [5,6]. For boolean functions f : f0; 1gn ! f0; 1g computable by AC0 circuits of depth d and size s, we show that P d−1 t (2) [ DTdepth(fRp) ≥ t ] ≤ (p · O(log s) ) for all p 2 [0; 1] and t 2 N.