Algebraic Methods for Interactive Proof Systems

Total Page:16

File Type:pdf, Size:1020Kb

Algebraic Methods for Interactive Proof Systems Algebraic Metho ds for Interactive Pro of Systems Carsten Lund y Lance Fortnow z Howard Karlo University of Chicago x Noam Nisan Hebrew University Abstract We present a new algebraic technique for the construction of interactive pro of systems We use our technique to prove that every language in the p olynomial time hierarchy has an interactive pro of system This technique played a pivotal role in the recent pro ofs that IPPSPACE S and that MIPNEXP BFL Intro duction NP can b e viewed as the set of languages L with this prop erty there is a deterministic p olynomialtime verier Vanna and an innitelyp owerful prover Pat such that for all x if x is in L then in p olynomial time Pat can p ersuade Vanna that x is in L and if x is not in L then no prover Pat or any other can p ersuade Vanna that x is in L Pat and Vanna communicate on a twoway channel though twoway communication is not necessary here For example Pat can convince Vanna that a graph G is colorable by exhibiting a coloring If G is not colorable no prover will ever succeed in p ersuading Vanna that G is colorable Of course coNPcomplete languages are not thought to b e in NP No prover is known who can convince a skeptical deterministic verier that G is not colorable if it is not colorable We can extend this idea of provability by allowing Vanna to ip coins and by requiring instead that if x is in L with probability at least Pat p ersuades Vanna that x is in L and if x is not in L no prover can convince Vanna that x is in L with probability more than Babai B and Goldwasser Micali and Racko GMR develop ed this interactive proof system mo del A summary of previous results on interactive pro of systems can b e found in BM While certain problems such as graph nonisomorphism which are not known to b e in NP were known to have interactive pro of systems GMW theoretical computer scientists generally b elieved that the class Supp orted by a fellowship from the University of Arhus y Supp orted by NSF grant CCR z Supp orted by NSF grant CCR x Some of this work was p erformed at MIT and supp orted by NSF grant CCR and ARO grant DLLK IP of languages accepted by interactive pro of systems was not much larger than NP In particular it was b elieved that coNPcomplete languages did not have interactive pro of systems We prove that interactive pro of systems have far greater p ower than originally b elieved Our main result is an interactive pro of system for the language fA sjs is the p ermanent of matrix Ag When combined with the fact that the p ermanent of matrices is Pcomplete V and the fact that P is hard for the p olynomialtime hierarchy T the existence of an interactive pro of system for the p ermanent implies that every language in the p olynomialtime hierarchy has an interactive pro of system In particular this means that every language in coNP has an interactive pro of system even the complement of COLORABILITY for example For the pro of we develop a new technique for reducing the problem of verifying the value of a lowdegree p olynomial at two given p oints to verifying the value at one new p oint Shamir S has used this technique to prove that all languages in PSPACE have interactive pro of systems From the fact that IPPSPACE F it follows that IPPSPACE Babai Fortnow and Lund BFL have also used this technique in their pro of that every language in nondeterministic exp onential time has a twoprover interactive pro of system in which the provers cannot communicate with one another Our results also have implications for program checking verication and selfcorrection in the context of Blum and Kannan BK Blum Luby and Rubinfeld BLR and Lipton L In fact the BlumLubyRubinfeld and Lipton pap ers inspired our result Our result do es not relativize Fortnow and Sipser FS have created an oracle under which coNP do es not have an interactive pro of system To our knowledge this is the rst result to go contrary to a previouslypublished oracle Subsequent to the announcement of our result Chor Goldreich and Hastad CGH proved the same relativized result for a random oracle Denitions A verier V is a p olynomialtime probabilistic Turing machine with a sp ecial communication tap e A prover P is an arbitrary map f from each nite sequence x q q q where x f g and each q f g to 1 2 3 i a string The computation pro ceeds as follows Both P and V get x f g V then computes for a while and writes a query q f g on her communication tap e P resp onds by replacing the q with f x q V 1 1 1 computes overwrites f x q with a query q f g and awaits P s resp onse f x q q This pro cess 1 2 1 2 continues until V halts and accepts or rejects x A round is a query from V followed by a resp onse from P The pair P V forms an interactive proof system for a language L if for all x f g 2 If x L then Pr V accepts input x when interacting with P 3 1 If x L then for all provers P PrV accepts x when interacting with P 3 IP is the class of all languages which have interactive pro of systems The class P consists of all functions f f g IN for which there exists a p olynomialtime nonde terministic Turing machine M such that for all inputs x the numb er of accepting computations of M on x P equals f x P is the class of languages recognized by a p olynomialtime oracle Turing machine with an oracle for some function f in P Given x the oracle Turing machine can learn f x in one time step by querying its oracle The Proto col We will prove P Theorem Every language in P has an interactive pro of system P Together with To das result that P contains all the languages of the p olynomialtime hierarchy T Theorem implies Corollary Every language in the p olynomialtime hierarchy has an interactive pro of system In partic ular every language in coNP has an interactive pro of system We list some facts ab out the p ermanent of a matrix A that will b e crucial in the pro of of Theorem If P a a a where the sum is over all p ermutations A a is r r the permanent p er A ij 1 (1) 2 (2) r (r ) P of f r g We can equivalently dene the p ermanent recursively as p er A a p er A 1i 1ji 1ir where A the iminor of A is the matrix A without the rst row and the ith column The numb er of 1ji p erfect matchings in an N b oy N girl bipartite graph G is equal to the p ermanent of Gs adjacency matrix We will exhibit an interactive pro of system for verifying the p ermanent of a matrix The following lemma implies that this is sucient to prove Theorem Lemma If L fA sjA is a matrix and p erA s g has an interactive pro of system then every P language in P has an interactive pro of system Pro of Sketch From the fact that computing the p ermanent of matrices is Pcomplete V we can P reduce the memb ership problem for a language L P to that of verifying the p ermanents of matrices Given an interactive pro of system for L it is easy to construct one for L Throughout most of this pap er we will work with the p ermanent over ZZ of an N N matrix A with p entries in ZZ where p is a prime in N N Bertrands Postulate NZ guarantees the existence of such p a prime If A is then the p ermanent of A over ZZ coincides with its p ermanent as an integer matrix p since the p ermanent of an N N matrix cannot exceed N We use the crucial fact that if B is an r r matrix over ZZ whose entries are linear p olynomials over ZZ then p er B is a p olynomial of degree at most p p r over ZZ Compared to p any r N is minuscule p The verier Vanna will use this fact to trip up a cheating prover She will maintain a list of pairs L B q B q B q where the B s are square matrices of the same size and q ZZ 1 1 2 2 t t i i p Initially L A s If s p er A then a prover who truthfully answers all of Vannas questions will induce Vanna eventually to shrink the list to a single pair B q where B is and q p er B At that p oint Vanna will correctly accept the input If s p er A then however the prover answers Vannas questions with very high probability Vanna will maintain this invariant the list contains at least one pair B q such that q p er B Invariant i i i i app ears in quotes b ecause with extremely low probability at some p oint every q might equal p er B i i When the list shrinks to one pair B q where B is and q p er B Vanna will reject the input if not earlier How Vanna manipulates the list is the crux of the proto col When L B q B b i j r ij and r for each i r Vanna constructs the minor B B asks Pat for the p ermanent of B i i 1ji P P r r and gets q in return Vanna checks that q b q if not she halts and rejects If q b q she i 1i i 1i i i=1 i=1 expands L by replacing L by B q B q B q Provided that q p er B q p er B for 1 1 2 2 r r i i some i When the list has more than one pair Vanna shrinks the list by replacing the rst two pairs C c D d by a new pair E e in the following way The function f x p er C xD C is a p olynomial of degree at most r over ZZ Vanna asks Pat for the r co ecients of f and constructs a p olynomial g from the p resp onses Or Vanna could just ask for the value of f at r arbitrary p oints and interp olate herself If g c or g d Vanna rejects 1 Vanna now uniformly cho oses a random a ZZ sends it to Pat constructs E C aD C and p e g a and replaces the pairs C c D d in L by the one pair E e The crucial fact is that if c p er C or d p er D then with probability at least r p p er E e This follows from Lemma Lemma Let C and D b e r r matrices over ZZ Let g b e a p olynomial of degree at most r over
Recommended publications
  • Database Theory
    DATABASE THEORY Lecture 4: Complexity of FO Query Answering Markus Krotzsch¨ TU Dresden, 21 April 2016 Overview 1. Introduction | Relational data model 2. First-order queries 3. Complexity of query answering 4. Complexity of FO query answering 5. Conjunctive queries 6. Tree-like conjunctive queries 7. Query optimisation 8. Conjunctive Query Optimisation / First-Order Expressiveness 9. First-Order Expressiveness / Introduction to Datalog 10. Expressive Power and Complexity of Datalog 11. Optimisation and Evaluation of Datalog 12. Evaluation of Datalog (2) 13. Graph Databases and Path Queries 14. Outlook: database theory in practice See course homepage [) link] for more information and materials Markus Krötzsch, 21 April 2016 Database Theory slide 2 of 41 How to Measure Query Answering Complexity Query answering as decision problem { consider Boolean queries Various notions of complexity: • Combined complexity (complexity w.r.t. size of query and database instance) • Data complexity (worst case complexity for any fixed query) • Query complexity (worst case complexity for any fixed database instance) Various common complexity classes: L ⊆ NL ⊆ P ⊆ NP ⊆ PSpace ⊆ ExpTime Markus Krötzsch, 21 April 2016 Database Theory slide 3 of 41 An Algorithm for Evaluating FO Queries function Eval(', I) 01 switch (') f I 02 case p(c1, ::: , cn): return hc1, ::: , cni 2 p 03 case : : return :Eval( , I) 04 case 1 ^ 2 : return Eval( 1, I) ^ Eval( 2, I) 05 case 9x. : 06 for c 2 ∆I f 07 if Eval( [x 7! c], I) then return true 08 g 09 return false 10 g Markus Krötzsch, 21 April 2016 Database Theory slide 4 of 41 FO Algorithm Worst-Case Runtime Let m be the size of ', and let n = jIj (total table sizes) • How many recursive calls of Eval are there? { one per subexpression: at most m • Maximum depth of recursion? { bounded by total number of calls: at most m • Maximum number of iterations of for loop? { j∆Ij ≤ n per recursion level { at most nm iterations I • Checking hc1, ::: , cni 2 p can be done in linear time w.r.t.
    [Show full text]
  • 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”.
    [Show full text]
  • Interactive Proof Systems and Alternating Time-Space Complexity
    Theoretical Computer Science 113 (1993) 55-73 55 Elsevier Interactive proof systems and alternating time-space complexity Lance Fortnow” and Carsten Lund** Department of Computer Science, Unicersity of Chicago. 1100 E. 58th Street, Chicago, IL 40637, USA Abstract Fortnow, L. and C. Lund, Interactive proof systems and alternating time-space complexity, Theoretical Computer Science 113 (1993) 55-73. We show a rough equivalence between alternating time-space complexity and a public-coin interactive proof system with the verifier having a polynomial-related time-space complexity. Special cases include the following: . All of NC has interactive proofs, with a log-space polynomial-time public-coin verifier vastly improving the best previous lower bound of LOGCFL for this model (Fortnow and Sipser, 1988). All languages in P have interactive proofs with a polynomial-time public-coin verifier using o(log’ n) space. l All exponential-time languages have interactive proof systems with public-coin polynomial-space exponential-time verifiers. To achieve better bounds, we show how to reduce a k-tape alternating Turing machine to a l-tape alternating Turing machine with only a constant factor increase in time and space. 1. Introduction In 1981, Chandra et al. [4] introduced alternating Turing machines, an extension of nondeterministic computation where the Turing machine can make both existential and universal moves. In 1985, Goldwasser et al. [lo] and Babai [l] introduced interactive proof systems, an extension of nondeterministic computation consisting of two players, an infinitely powerful prover and a probabilistic polynomial-time verifier. The prover will try to convince the verifier of the validity of some statement.
    [Show full text]
  • Complexity Theory Lecture 9 Co-NP Co-NP-Complete
    Complexity Theory 1 Complexity Theory 2 co-NP Complexity Theory Lecture 9 As co-NP is the collection of complements of languages in NP, and P is closed under complementation, co-NP can also be characterised as the collection of languages of the form: ′ L = x y y <p( x ) R (x, y) { |∀ | | | | → } Anuj Dawar University of Cambridge Computer Laboratory NP – the collection of languages with succinct certificates of Easter Term 2010 membership. co-NP – the collection of languages with succinct certificates of http://www.cl.cam.ac.uk/teaching/0910/Complexity/ disqualification. Anuj Dawar May 14, 2010 Anuj Dawar May 14, 2010 Complexity Theory 3 Complexity Theory 4 NP co-NP co-NP-complete P VAL – the collection of Boolean expressions that are valid is co-NP-complete. Any language L that is the complement of an NP-complete language is co-NP-complete. Any of the situations is consistent with our present state of ¯ knowledge: Any reduction of a language L1 to L2 is also a reduction of L1–the complement of L1–to L¯2–the complement of L2. P = NP = co-NP • There is an easy reduction from the complement of SAT to VAL, P = NP co-NP = NP = co-NP • ∩ namely the map that takes an expression to its negation. P = NP co-NP = NP = co-NP • ∩ VAL P P = NP = co-NP ∈ ⇒ P = NP co-NP = NP = co-NP • ∩ VAL NP NP = co-NP ∈ ⇒ Anuj Dawar May 14, 2010 Anuj Dawar May 14, 2010 Complexity Theory 5 Complexity Theory 6 Prime Numbers Primality Consider the decision problem PRIME: Another way of putting this is that Composite is in NP.
    [Show full text]
  • If Np Languages Are Hard on the Worst-Case, Then It Is Easy to Find Their Hard Instances
    IF NP LANGUAGES ARE HARD ON THE WORST-CASE, THEN IT IS EASY TO FIND THEIR HARD INSTANCES Dan Gutfreund, Ronen Shaltiel, and Amnon Ta-Shma Abstract. We prove that if NP 6⊆ BPP, i.e., if SAT is worst-case hard, then for every probabilistic polynomial-time algorithm trying to decide SAT, there exists some polynomially samplable distribution that is hard for it. That is, the algorithm often errs on inputs from this distribution. This is the ¯rst worst-case to average-case reduction for NP of any kind. We stress however, that this does not mean that there exists one ¯xed samplable distribution that is hard for all probabilistic polynomial-time algorithms, which is a pre-requisite assumption needed for one-way func- tions and cryptography (even if not a su±cient assumption). Neverthe- less, we do show that there is a ¯xed distribution on instances of NP- complete languages, that is samplable in quasi-polynomial time and is hard for all probabilistic polynomial-time algorithms (unless NP is easy in the worst case). Our results are based on the following lemma that may be of independent interest: Given the description of an e±cient (probabilistic) algorithm that fails to solve SAT in the worst case, we can e±ciently generate at most three Boolean formulae (of increasing lengths) such that the algorithm errs on at least one of them. Keywords. Average-case complexity, Worst-case to average-case re- ductions, Foundations of cryptography, Pseudo classes Subject classi¯cation. 68Q10 (Modes of computation (nondetermin- istic, parallel, interactive, probabilistic, etc.) 68Q15 Complexity classes (hierarchies, relations among complexity classes, etc.) 68Q17 Compu- tational di±culty of problems (lower bounds, completeness, di±culty of approximation, etc.) 94A60 Cryptography 2 Gutfreund, Shaltiel & Ta-Shma 1.
    [Show full text]
  • 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.
    [Show full text]
  • Computational Complexity and Intractability: an Introduction to the Theory of NP Chapter 9 2 Objectives
    1 Computational Complexity and Intractability: An Introduction to the Theory of NP Chapter 9 2 Objectives . Classify problems as tractable or intractable . Define decision problems . Define the class P . Define nondeterministic algorithms . Define the class NP . Define polynomial transformations . Define the class of NP-Complete 3 Input Size and Time Complexity . Time complexity of algorithms: . Polynomial time (efficient) vs. Exponential time (inefficient) f(n) n = 10 30 50 n 0.00001 sec 0.00003 sec 0.00005 sec n5 0.1 sec 24.3 sec 5.2 mins 2n 0.001 sec 17.9 mins 35.7 yrs 4 “Hard” and “Easy” Problems . “Easy” problems can be solved by polynomial time algorithms . Searching problem, sorting, Dijkstra’s algorithm, matrix multiplication, all pairs shortest path . “Hard” problems cannot be solved by polynomial time algorithms . 0/1 knapsack, traveling salesman . Sometimes the dividing line between “easy” and “hard” problems is a fine one. For example, . Find the shortest path in a graph from X to Y (easy) . Find the longest path (with no cycles) in a graph from X to Y (hard) 5 “Hard” and “Easy” Problems . Motivation: is it possible to efficiently solve “hard” problems? Efficiently solve means polynomial time solutions. Some problems have been proved that no efficient algorithms for them. For example, print all permutation of a number n. However, many problems we cannot prove there exists no efficient algorithms, and at the same time, we cannot find one either. 6 Traveling Salesperson Problem . No algorithm has ever been developed with a Worst-case time complexity better than exponential .
    [Show full text]
  • 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).
    [Show full text]
  • Computational Complexity: a Modern Approach
    i Computational Complexity: A Modern Approach Draft of a book: Dated January 2007 Comments welcome! Sanjeev Arora and Boaz Barak Princeton University [email protected] Not to be reproduced or distributed without the authors’ permission This is an Internet draft. Some chapters are more finished than others. References and attributions are very preliminary and we apologize in advance for any omissions (but hope you will nevertheless point them out to us). Please send us bugs, typos, missing references or general comments to [email protected] — Thank You!! DRAFT ii DRAFT Chapter 9 Complexity of counting “It is an empirical fact that for many combinatorial problems the detection of the existence of a solution is easy, yet no computationally efficient method is known for counting their number.... for a variety of problems this phenomenon can be explained.” L. Valiant 1979 The class NP captures the difficulty of finding certificates. However, in many contexts, one is interested not just in a single certificate, but actually counting the number of certificates. This chapter studies #P, (pronounced “sharp p”), a complexity class that captures this notion. Counting problems arise in diverse fields, often in situations having to do with estimations of probability. Examples include statistical estimation, statistical physics, network design, and more. Counting problems are also studied in a field of mathematics called enumerative combinatorics, which tries to obtain closed-form mathematical expressions for counting problems. To give an example, in the 19th century Kirchoff showed how to count the number of spanning trees in a graph using a simple determinant computation. Results in this chapter will show that for many natural counting problems, such efficiently computable expressions are unlikely to exist.
    [Show full text]
  • Chapter 24 Conp, Self-Reductions
    Chapter 24 coNP, Self-Reductions CS 473: Fundamental Algorithms, Spring 2013 April 24, 2013 24.1 Complementation and Self-Reduction 24.2 Complementation 24.2.1 Recap 24.2.1.1 The class P (A) A language L (equivalently decision problem) is in the class P if there is a polynomial time algorithm A for deciding L; that is given a string x, A correctly decides if x 2 L and running time of A on x is polynomial in jxj, the length of x. 24.2.1.2 The class NP Two equivalent definitions: (A) Language L is in NP if there is a non-deterministic polynomial time algorithm A (Turing Machine) that decides L. (A) For x 2 L, A has some non-deterministic choice of moves that will make A accept x (B) For x 62 L, no choice of moves will make A accept x (B) L has an efficient certifier C(·; ·). (A) C is a polynomial time deterministic algorithm (B) For x 2 L there is a string y (proof) of length polynomial in jxj such that C(x; y) accepts (C) For x 62 L, no string y will make C(x; y) accept 1 24.2.1.3 Complementation Definition 24.2.1. Given a decision problem X, its complement X is the collection of all instances s such that s 62 L(X) Equivalently, in terms of languages: Definition 24.2.2. Given a language L over alphabet Σ, its complement L is the language Σ∗ n L. 24.2.1.4 Examples (A) PRIME = nfn j n is an integer and n is primeg o PRIME = n n is an integer and n is not a prime n o PRIME = COMPOSITE .
    [Show full text]
  • Succinctness of the Complement and Intersection of Regular Expressions Wouter Gelade, Frank Neven
    Succinctness of the Complement and Intersection of Regular Expressions Wouter Gelade, Frank Neven To cite this version: Wouter Gelade, Frank Neven. Succinctness of the Complement and Intersection of Regular Expres- sions. STACS 2008, Feb 2008, Bordeaux, France. pp.325-336. hal-00226864 HAL Id: hal-00226864 https://hal.archives-ouvertes.fr/hal-00226864 Submitted on 30 Jan 2008 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Symposium on Theoretical Aspects of Computer Science 2008 (Bordeaux), pp. 325-336 www.stacs-conf.org SUCCINCTNESS OF THE COMPLEMENT AND INTERSECTION OF REGULAR EXPRESSIONS WOUTER GELADE AND FRANK NEVEN Hasselt University and Transnational University of Limburg, School for Information Technology E-mail address: [email protected] Abstract. We study the succinctness of the complement and intersection of regular ex- pressions. In particular, we show that when constructing a regular expression defining the complement of a given regular expression, a double exponential size increase cannot be avoided. Similarly, when constructing a regular expression defining the intersection of a fixed and an arbitrary number of regular expressions, an exponential and double expo- nential size increase, respectively, can in worst-case not be avoided.
    [Show full text]
  • 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/.
    [Show full text]