A Clari Cation Concerning the L Hierarchy 1 the Proof
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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. -
On Uniformity Within NC
On Uniformity Within NC David A Mix Barrington Neil Immerman HowardStraubing University of Massachusetts University of Massachusetts Boston Col lege Journal of Computer and System Science Abstract In order to study circuit complexity classes within NC in a uniform setting we need a uniformity condition which is more restrictive than those in common use Twosuch conditions stricter than NC uniformity RuCo have app eared in recent research Immermans families of circuits dened by rstorder formulas ImaImb and a unifor mity corresp onding to Buss deterministic logtime reductions Bu We show that these two notions are equivalent leading to a natural notion of uniformity for lowlevel circuit complexity classes Weshow that recent results on the structure of NC Ba still hold true in this very uniform setting Finallyweinvestigate a parallel notion of uniformity still more restrictive based on the regular languages Here we givecharacterizations of sub classes of the regular languages based on their logical expressibility extending recentwork of Straubing Therien and Thomas STT A preliminary version of this work app eared as BIS Intro duction Circuit Complexity Computer scientists have long tried to classify problems dened as Bo olean predicates or functions by the size or depth of Bo olean circuits needed to solve them This eort has Former name David A Barrington Supp orted by NSF grant CCR Mailing address Dept of Computer and Information Science U of Mass Amherst MA USA Supp orted by NSF grants DCR and CCR Mailing address Dept of -
Nitin Saurabh the Institute of Mathematical Sciences, Chennai
ALGEBRAIC MODELS OF COMPUTATION By Nitin Saurabh The Institute of Mathematical Sciences, Chennai. A thesis submitted to the Board of Studies in Mathematical Sciences In partial fulllment of the requirements For the Degree of Master of Science of HOMI BHABHA NATIONAL INSTITUTE April 2012 CERTIFICATE Certied that the work contained in the thesis entitled Algebraic models of Computation, by Nitin Saurabh, has been carried out under my supervision and that this work has not been submitted elsewhere for a degree. Meena Mahajan Theoretical Computer Science Group The Institute of Mathematical Sciences, Chennai ACKNOWLEDGEMENTS I would like to thank my advisor Prof. Meena Mahajan for her invaluable guidance and continuous support since my undergraduate days. Her expertise and ideas helped me comprehend new techniques. Her guidance during the preparation of this thesis has been invaluable. I also thank her for always being there to discuss and clarify any matter. I am extremely grateful to all the faculty members of theory group at IMSc and CMI for their continuous encouragement and giving me an opportunity to learn from them. I would like to thank all my friends, at IMSc and CMI, for making my stay in Chennai a memorable one. Most of all, I take this opportunity to thank my parents, my uncle and my brother. Abstract Valiant [Val79, Val82] had proposed an analogue of the theory of NP-completeness in an entirely algebraic framework to study the complexity of polynomial families. Artihmetic circuits form the most standard model for studying the complexity of polynomial computations. In a note [Val92], Valiant argued that in order to prove lower bounds for boolean circuits, obtaining lower bounds for arithmetic circuits should be a rst step. -
On the NP-Completeness of the Minimum Circuit Size Problem
On the NP-Completeness of the Minimum Circuit Size Problem John M. Hitchcock∗ A. Pavany Department of Computer Science Department of Computer Science University of Wyoming Iowa State University Abstract We study the Minimum Circuit Size Problem (MCSP): given the truth-table of a Boolean function f and a number k, does there exist a Boolean circuit of size at most k computing f? This is a fundamental NP problem that is not known to be NP-complete. Previous work has studied consequences of the NP-completeness of MCSP. We extend this work and consider whether MCSP may be complete for NP under more powerful reductions. We also show that NP-completeness of MCSP allows for amplification of circuit complexity. We show the following results. • If MCSP is NP-complete via many-one reductions, the following circuit complexity amplifi- Ω(1) cation result holds: If NP\co-NP requires 2n -size circuits, then ENP requires 2Ω(n)-size circuits. • If MCSP is NP-complete under truth-table reductions, then EXP 6= NP \ SIZE(2n ) for some > 0 and EXP 6= ZPP. This result extends to polylog Turing reductions. 1 Introduction Many natural NP problems are known to be NP-complete. Ladner's theorem [14] tells us that if P is different from NP, then there are NP-intermediate problems: problems that are in NP, not in P, but also not NP-complete. The examples arising out of Ladner's theorem come from diagonalization and are not natural. A canonical candidate example of a natural NP-intermediate problem is the Graph Isomorphism (GI) problem. -
Week 1: an Overview of Circuit Complexity 1 Welcome 2
Topics in Circuit Complexity (CS354, Fall’11) Week 1: An Overview of Circuit Complexity Lecture Notes for 9/27 and 9/29 Ryan Williams 1 Welcome The area of circuit complexity has a long history, starting in the 1940’s. It is full of open problems and frontiers that seem insurmountable, yet the literature on circuit complexity is fairly large. There is much that we do know, although it is scattered across several textbooks and academic papers. I think now is a good time to look again at circuit complexity with fresh eyes, and try to see what can be done. 2 Preliminaries An n-bit Boolean function has domain f0; 1gn and co-domain f0; 1g. At a high level, the basic question asked in circuit complexity is: given a collection of “simple functions” and a target Boolean function f, how efficiently can f be computed (on all inputs) using the simple functions? Of course, efficiency can be measured in many ways. The most natural measure is that of the “size” of computation: how many copies of these simple functions are necessary to compute f? Let B be a set of Boolean functions, which we call a basis set. The fan-in of a function g 2 B is the number of inputs that g takes. (Typical choices are fan-in 2, or unbounded fan-in, meaning that g can take any number of inputs.) We define a circuit C with n inputs and size s over a basis B, as follows. C consists of a directed acyclic graph (DAG) of s + n + 2 nodes, with n sources and one sink (the sth node in some fixed topological order on the nodes). -
The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. -
UNIONS of LINES in Fn 1. Introduction the Main Problem We
UNIONS OF LINES IN F n RICHARD OBERLIN Abstract. We show that if a collection of lines in a vector space over a finite field has \dimension" at least 2(d−1)+β; then its union has \dimension" at least d + β: This is the sharp estimate of its type when no structural assumptions are placed on the collection of lines. We also consider some refinements and extensions of the main result, including estimates for unions of k-planes. 1. Introduction The main problem we will consider here is to give a lower bound for the dimension of the union of a collection of lines in terms of the dimension of the collection of lines, without imposing a structural hy- pothesis on the collection (in contrast to the Kakeya problem where one assumes that the lines are direction-separated, or perhaps satisfy the weaker \Wolff axiom"). Specifically, we are motivated by the following conjecture of D. Ober- lin (hdim denotes Hausdorff dimension). Conjecture 1.1. Suppose d ≥ 1 is an integer, that 0 ≤ β ≤ 1; and that L is a collection of lines in Rn with hdim(L) ≥ 2(d − 1) + β: Then [ (1) hdim( L) ≥ d + β: L2L The bound (1), if true, would be sharp, as one can see by taking L to be the set of lines contained in the d-planes belonging to a β- dimensional family of d-planes. (Furthermore, there is nothing to be gained by taking 1 < β ≤ 2 since the dimension of the set of lines contained in a d + 1-plane is 2(d − 1) + 2.) Standard Fourier-analytic methods show that (1) holds for d = 1, but the conjecture is open for d > 1: As a model problem, one may consider an analogous question where Rn is replaced by a vector space over a finite-field. -
Collapsing Exact Arithmetic Hierarchies
Electronic Colloquium on Computational Complexity, Report No. 131 (2013) Collapsing Exact Arithmetic Hierarchies Nikhil Balaji and Samir Datta Chennai Mathematical Institute fnikhil,[email protected] Abstract. We provide a uniform framework for proving the collapse of the hierarchy, NC1(C) for an exact arith- metic class C of polynomial degree. These hierarchies collapses all the way down to the third level of the AC0- 0 hierarchy, AC3(C). Our main collapsing exhibits are the classes 1 1 C 2 fC=NC ; C=L; C=SAC ; C=Pg: 1 1 NC (C=L) and NC (C=P) are already known to collapse [1,18,19]. We reiterate that our contribution is a framework that works for all these hierarchies. Our proof generalizes a proof 0 1 from [8] where it is used to prove the collapse of the AC (C=NC ) hierarchy. It is essentially based on a polynomial degree characterization of each of the base classes. 1 Introduction Collapsing hierarchies has been an important activity for structural complexity theorists through the years [12,21,14,23,18,17,4,11]. We provide a uniform framework for proving the collapse of the NC1 hierarchy over an exact arithmetic class. Using 0 our method, such a hierarchy collapses all the way down to the AC3 closure of the class. 1 1 1 Our main collapsing exhibits are the NC hierarchies over the classes C=NC , C=L, C=SAC , C=P. Two of these 1 1 hierarchies, viz. NC (C=L); NC (C=P), are already known to collapse ([1,19,18]) while a weaker collapse is known 0 1 for a third one viz. -
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 ). -
Two-Way Automata Characterizations of L/Poly Versus NL
Two-way automata characterizations of L/poly versus NL Christos A. Kapoutsis1;? and Giovanni Pighizzini2 1 LIAFA, Universit´eParis VII, France 2 DICo, Universit`adegli Studi di Milano, Italia Abstract. Let L/poly and NL be the standard complexity classes, of languages recognizable in logarithmic space by Turing machines which are deterministic with polynomially-long advice and nondeterministic without advice, respectively. We recast the question whether L/poly ⊇ NL in terms of deterministic and nondeterministic two-way finite automata (2dfas and 2nfas). We prove it equivalent to the question whether every s-state unary 2nfa has an equivalent poly(s)-state 2dfa, or whether a poly(h)-state 2dfa can check accessibility in h-vertex graphs (even under unary encoding) or check two-way liveness in h-tall, h-column graphs. This complements two recent improvements of an old theorem of Berman and Lingas. On the way, we introduce new types of reductions between regular languages (even unary ones), use them to prove the completeness of specific languages for two-way nondeterministic polynomial size, and propose a purely combinatorial conjecture that implies L/poly + NL. 1 Introduction A prominent open question in complexity theory asks whether nondeterminism is essential in logarithmic-space Turing machines. Formally, this is the question whether L = NL, for L and NL the standard classes of languages recognizable by logarithmic-space deterministic and nondeterministic Turing machines. In the late 70's, Berman and Lingas [1] connected this question to the comparison between deterministic and nondeterministic two-way finite automata (2dfas and 2nfas), proving that if L = NL, then for every s-state σ-symbol 2nfa there is a poly(sσ)-state 2dfa which agrees with it on all inputs of length ≤ s. -
The Boolean Formula Value Problem Is in ALOGTIME (Preliminary Version)
The Boolean formula value problem is in ALOGTIME (Preliminary Version) Samuel R. Buss¤ Department of Mathematics University of California, Berkeley January 1987 Abstract The Boolean formula value problem is in alternating log time and, more generally, parenthesis context-free languages are in alternating log time. The evaluation of reverse Polish notation Boolean formulas is also in alternating log time. These results are optimal since the Boolean formula value problem is complete for alternating log time under deterministic log time reductions. Consequently, it is also complete for alternating log time under AC0 reductions. 1. Introduction The Boolean formula value problem is to determine the truth value of a variable-free Boolean formula, or equivalently, to recognize the true Boolean sentences. N. Lynch [11] gave log space algorithms for the Boolean formula value problem and for the more general problem of recognizing a parenthesis context-free grammar. This paper shows that these problems have alternating log time algorithms. This answers the question of Cook [5] of whether the Boolean formula value problem is log space complete | it is not, unless log space and alternating log time are identical. Our results are optimal since, for an appropriately de¯ned notion of log time reductions, the Boolean formula value problem is complete for alternating log time under deterministic log time reductions; consequently, it is also complete for alternating log time under AC0 reductions. It follows that the Boolean formula value problem is not in the log time hierarchy. There are two reasons why the Boolean formula value problem is interesting. First, a Boolean (or propositional) formula is a very fundamental concept ¤Supported in part by an NSF postdoctoral fellowship.