On Uniformity and Circuit Lower Bounds
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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. -
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”. -
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. -
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/. -
Lecture 11 1 Non-Uniform Complexity
Notes on Complexity Theory Last updated: October, 2011 Lecture 11 Jonathan Katz 1 Non-Uniform Complexity 1.1 Circuit Lower Bounds for a Language in §2 \ ¦2 We have seen that there exist \very hard" languages (i.e., languages that require circuits of size (1 ¡ ")2n=n). If we can show that there exists a language in NP that is even \moderately hard" (i.e., requires circuits of super-polynomial size) then we will have proved P 6= NP. (In some sense, it would be even nicer to show some concrete language in NP that requires circuits of super-polynomial size. But mere existence of such a language is enough.) c Here we show that for every c there is a language in §2 \ ¦2 that is not in size(n ). Note that this does not prove §2 \ ¦2 6⊆ P=poly since, for every c, the language we obtain is di®erent. (Indeed, using the time hierarchy theorem, we have that for every c there is a language in P that is not in time(nc).) What is particularly interesting here is that (1) we prove a non-uniform lower bound and (2) the proof is, in some sense, rather simple. c Theorem 1 For every c, there is a language in §4 \ ¦4 that is not in size(n ). Proof Fix some c. For each n, let Cn be the lexicographically ¯rst circuit on n inputs such c that (the function computed by) Cn cannot be computed by any circuit of size at most n . By the c+1 non-uniform hierarchy theorem (see [1]), there exists such a Cn of size at most n (for n large c enough). -
Simple Doubly-Efficient Interactive Proof Systems for Locally
Electronic Colloquium on Computational Complexity, Revision 3 of Report No. 18 (2017) Simple doubly-efficient interactive proof systems for locally-characterizable sets Oded Goldreich∗ Guy N. Rothblumy September 8, 2017 Abstract A proof system is called doubly-efficient if the prescribed prover strategy can be implemented in polynomial-time and the verifier’s strategy can be implemented in almost-linear-time. We present direct constructions of doubly-efficient interactive proof systems for problems in P that are believed to have relatively high complexity. Specifically, such constructions are presented for t-CLIQUE and t-SUM. In addition, we present a generic construction of such proof systems for a natural class that contains both problems and is in NC (and also in SC). The proof systems presented by us are significantly simpler than the proof systems presented by Goldwasser, Kalai and Rothblum (JACM, 2015), let alone those presented by Reingold, Roth- blum, and Rothblum (STOC, 2016), and can be implemented using a smaller number of rounds. Contents 1 Introduction 1 1.1 The current work . 1 1.2 Relation to prior work . 3 1.3 Organization and conventions . 4 2 Preliminaries: The sum-check protocol 5 3 The case of t-CLIQUE 5 4 The general result 7 4.1 A natural class: locally-characterizable sets . 7 4.2 Proof of Theorem 1 . 8 4.3 Generalization: round versus computation trade-off . 9 4.4 Extension to a wider class . 10 5 The case of t-SUM 13 References 15 Appendix: An MA proof system for locally-chracterizable sets 18 ∗Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel. -
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 ). -
Lower Bounds from Learning Algorithms
Circuit Lower Bounds from Nontrivial Learning Algorithms Igor C. Oliveira Rahul Santhanam Charles University in Prague University of Oxford Motivation and Background Previous Work Lemma 1 [Speedup Phenomenon in Learning Theory]. From PSPACE BPTIME[exp(no(1))], simple padding argument implies: DSPACE[nω(1)] BPEXP. Some connections between algorithms and circuit lower bounds: Assume C[poly(n)] can be (weakly) learned in time 2n/nω(1). Lower bounds Lemma [Diagonalization] (3) (the proof is sketched later). “Fast SAT implies lower bounds” [KL’80] against C ? Let k N and ε > 0 be arbitrary constants. There is L DSPACE[nω(1)] that is not in C[poly]. “Nontrivial” If Circuit-SAT can be solved efficiently then EXP ⊈ P/poly. learning algorithm Then C-circuits of size nk can be learned to accuracy n-k in Since DSPACE[nω(1)] BPEXP, we get BPEXP C[poly], which for a circuit class C “Derandomization implies lower bounds” [KI’03] time at most exp(nε). completes the proof of Theorem 1. If PIT NSUBEXP then either (i) NEXP ⊈ P/poly; or 0 0 Improved algorithmic ACC -SAT ACC -Learning It remains to prove the following lemmas. upper bounds ? (ii) Permanent is not computed by poly-size arithmetic circuits. (1) Speedup Lemma (relies on recent work [CIKK’16]). “Nontrivial SAT implies lower bounds” [Wil’10] Nontrivial: 2n/nω(1) ? (Non-uniform) Circuit Classes: If Circuit-SAT for poly-size circuits can be solved in time (2) PSPACE Simulation Lemma (follows [KKO’13]). 2n/nω(1) then NEXP ⊈ P/poly. SETH: 2(1-ε)n ? ? (3) Diagonalization Lemma [Folklore]. -
Multiparty Communication Complexity and Threshold Circuit Size of AC^0
2009 50th Annual IEEE Symposium on Foundations of Computer Science Multiparty Communication Complexity and Threshold Circuit Size of AC0 Paul Beame∗ Dang-Trinh Huynh-Ngoc∗y Computer Science and Engineering Computer Science and Engineering University of Washington University of Washington Seattle, WA 98195-2350 Seattle, WA 98195-2350 [email protected] [email protected] Abstract— We prove an nΩ(1)=4k lower bound on the random- that the Generalized Inner Product function in ACC0 requires ized k-party communication complexity of depth 4 AC0 functions k-party NOF communication complexity Ω(n=4k) which is in the number-on-forehead (NOF) model for up to Θ(log n) polynomial in n for k up to Θ(log n). players. These are the first non-trivial lower bounds for general 0 NOF multiparty communication complexity for any AC0 function However, for AC functions much less has been known. for !(log log n) players. For non-constant k the bounds are larger For the communication complexity of the set disjointness than all previous lower bounds for any AC0 function even for function with k players (which is in AC0) there are lower simultaneous communication complexity. bounds of the form Ω(n1=(k−1)=(k−1)) in the simultaneous Our lower bounds imply the first superpolynomial lower bounds NOF [24], [5] and nΩ(1=k)=kO(k) in the one-way NOF for the simulation of AC0 by MAJ ◦ SYMM ◦ AND circuits, showing that the well-known quasipolynomial simulations of AC0 model [26]. These are sub-polynomial lower bounds for all by such circuits are qualitatively optimal, even for formulas of non-constant values of k and, at best, polylogarithmic when small constant depth. -
Lecture 13: NP-Completeness
Lecture 13: NP-completeness Valentine Kabanets November 3, 2016 1 Polytime mapping-reductions We say that A is polytime reducible to B if there is a polytime computable function f (a reduction) such that, for every string x, x 2 A iff f(x) 2 B. We use the notation \A ≤p B." (This is the same as a notion of mapping reduction from Computability we saw earlier, with the only change being that the reduction f be polytime computable.) It is easy to see Theorem 1. If A ≤p B and B 2 P, then A 2 P. 2 NP-completeness 2.1 Definitions A language B is NP-complete if 1. B is in NP, and 2. every language A in NP is polytime reducible to B (i.e., A ≤p B). In words, an NP-complete problem is the \hardest" problem in the class NP. It is easy to see the following: Theorem 2. If B is NP-complete and B is in P, then NP = P. It is also possible to show (Exercise!) that Theorem 3. If B is NP-complete and B ≤p C for some C in NP, then C is NP-complete. 2.2 \Trivial" NP-complete problem The following \scaled down version of ANTM " is NP-complete. p t ANTM = fhM; w; 1 i j NTM M accepts w within t stepsg p Theorem 4. The language ANTM defined above is NP-complete. 1 p Proof. First, ANTM is in NP, as we can always simulate a given nondeterministic TM M on a given input w for t steps, so that our simulation takes time poly(jhMij; jwj; t) (polynomial in the input size). -
A Superpolynomial Lower Bound on the Size of Uniform Non-Constant-Depth Threshold Circuits for the Permanent
A Superpolynomial Lower Bound on the Size of Uniform Non-constant-depth Threshold Circuits for the Permanent Pascal Koiran Sylvain Perifel LIP LIAFA Ecole´ Normale Superieure´ de Lyon Universite´ Paris Diderot – Paris 7 Lyon, France Paris, France [email protected] [email protected] Abstract—We show that the permanent cannot be computed speak of DLOGTIME-uniformity), Allender [1] (see also by DLOGTIME-uniform threshold or arithmetic circuits of similar results on circuits with modulo gates in [2]) has depth o(log log n) and polynomial size. shown that the permanent does not have threshold circuits of Keywords-permanent; lower bound; threshold circuits; uni- constant depth and “sub-subexponential” size. In this paper, form circuits; non-constant depth circuits; arithmetic circuits we obtain a tradeoff between size and depth: instead of sub-subexponential size, we only prove a superpolynomial lower bound on the size of the circuits, but now the depth I. INTRODUCTION is no more constant. More precisely, we show the following Both in Boolean and algebraic complexity, the permanent theorem. has proven to be a central problem and showing lower Theorem 1: The permanent does not have DLOGTIME- bounds on its complexity has become a major challenge. uniform polynomial-size threshold circuits of depth This central position certainly comes, among others, from its o(log log n). ]P-completeness [15], its VNP-completeness [14], and from It seems to be the first superpolynomial lower bound on Toda’s theorem stating that the permanent is as powerful the size of non-constant-depth threshold circuits for the as the whole polynomial hierarchy [13].