Packing Trominoes Is NP-Complete, #P-Complete and ASP-Complete
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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. -
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. -
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. -
The Complexity of Space Bounded Interactive Proof Systems
The Complexity of Space Bounded Interactive Proof Systems ANNE CONDON Computer Science Department, University of Wisconsin-Madison 1 INTRODUCTION Some of the most exciting developments in complexity theory in recent years concern the complexity of interactive proof systems, defined by Goldwasser, Micali and Rackoff (1985) and independently by Babai (1985). In this paper, we survey results on the complexity of space bounded interactive proof systems and their applications. An early motivation for the study of interactive proof systems was to extend the notion of NP as the class of problems with efficient \proofs of membership". Informally, a prover can convince a verifier in polynomial time that a string is in an NP language, by presenting a witness of that fact to the verifier. Suppose that the power of the verifier is extended so that it can flip coins and can interact with the prover during the course of a proof. In this way, a verifier can gather statistical evidence that an input is in a language. As we will see, the interactive proof system model precisely captures this in- teraction between a prover P and a verifier V . In the model, the computation of V is probabilistic, but is typically restricted in time or space. A language is accepted by the interactive proof system if, for all inputs in the language, V accepts with high probability, based on the communication with the \honest" prover P . However, on inputs not in the language, V rejects with high prob- ability, even when communicating with a \dishonest" prover. In the general model, V can keep its coin flips secret from the prover. -
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model Kevin Sim Emma Hart Institute for Informatics and Digital Innovation Institute for Informatics and Digital Innovation Edinburgh Napier University Edinburgh Napier University Edinburgh, Scotland, UK Edinburgh, Scotland, UK [email protected] [email protected] ABSTRACT exhibit superior performance than similar human designed Novel deterministic heuristics are generated using Single Node heuristics and can be used to produce collections of heuris- Genetic Programming for application to the One Dimen- tics that collectively maximise the potential of selective HH. sional Bin Packing Problem. First a single deterministic The papers contribution is two fold. Single heuristics ca- heuristic was evolved that minimised the total number of pable of outperforming a range of well researched deter- bins used when applied to a set of 685 training instances. ministic heuristics are generated by combining features ex- Following this, a set of heuristics were evolved using a form tracted from those heuristics. Secondly an island model[19] of cooperative co-evolution that collectively minimise the is adapted to use multiple SNGP implementations to gener- number of bins used across the same set of problems. Re- ate diverse sets of heuristics which collectively outperform sults on an unseen test set comprising a further 685 prob- any of the single heuristics when used in isolation. Both ap- lem instances show that the single evolved heuristic out- proaches are trained and evaluated using equal divisions of a performs existing deterministic heuristics described in the large set of 1370 benchmark problem instances sourced from literature. -
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/. -
13. Mathematics University of Central Oklahoma
Southwestern Oklahoma State University SWOSU Digital Commons Oklahoma Research Day Abstracts 2013 Oklahoma Research Day Jan 10th, 12:00 AM 13. Mathematics University of Central Oklahoma Follow this and additional works at: https://dc.swosu.edu/ordabstracts Part of the Animal Sciences Commons, Biology Commons, Chemistry Commons, Computer Sciences Commons, Environmental Sciences Commons, Mathematics Commons, and the Physics Commons University of Central Oklahoma, "13. Mathematics" (2013). Oklahoma Research Day Abstracts. 12. https://dc.swosu.edu/ordabstracts/2013oklahomaresearchday/mathematicsandscience/12 This Event is brought to you for free and open access by the Oklahoma Research Day at SWOSU Digital Commons. It has been accepted for inclusion in Oklahoma Research Day Abstracts by an authorized administrator of SWOSU Digital Commons. An ADA compliant document is available upon request. For more information, please contact [email protected]. Abstracts from the 2013 Oklahoma Research Day Held at the University of Central Oklahoma 05. Mathematics and Science 13. Mathematics 05.13.01 A simplified proof of the Kantorovich theorem for solving equations using scalar telescopic series Ioannis Argyros, Cameron University The Kantorovich theorem is an important tool in Mathematical Analysis for solving nonlinear equations in abstract spaces by approximating a locally unique solution using the popular Newton-Kantorovich method.Many proofs have been given for this theorem using techniques such as the contraction mapping principle,majorizing sequences, recurrent functions and other techniques.These methods are rather long,complicated and not very easy to understand in general by undergraduate students.In the present paper we present a proof using simple telescopic series studied first in a Calculus II class. -
CS 579: Computational Complexity. Lecture 2 Space Complexity
CS 579: Computational Complexity. Lecture 2 Space complexity. Alexandra Kolla Today Space Complexity, L,NL Configuration Graphs Log- Space Reductions NL Completeness, STCONN Savitch’s theorem SL Turing machines, briefly (3-tape) Turing machine M described by tuple (Γ,Q,δ), where ◦ Γ is “alphabet” . Contains start and blank symbol, 0,1,among others (constant size). ◦ Q is set of states, including designated starting state and halt state (constant size). ◦ Transition function δ:푄 × Γ3 → 푄 × Γ2 × {퐿, 푆, 푅}3 describing the rules M uses to move. Turing machines, briefly (3-tape) NON-DETERMINISTIC Turing machine M described by tuple (Γ,Q,δ0,δ1), where ◦ Γ is “alphabet” . Contains start and blank symbol, 0,1,among others (constant size). ◦ Q is set of states, including designated starting state and halt state (constant size). ◦ Two transition functions δ0, δ1 :푄 × Γ3 → 푄 × Γ2 × {퐿, 푆, 푅}3. At every step, TM makes non- deterministic choice which one to Space bounded turing machines Space-bounded turing machines used to study memory requirements of computational tasks. Definition. Let 푠: ℕ → ℕ and 퐿 ⊆ {0,1}∗. We say that L∈ SPACE(s(n)) if there is a constant c and a TM M deciding L s.t. at most c∙s(n) locations on M’s work tapes (excluding the input tape) are ever visited by M’s head during its computation on every input of length n. We will assume a single work tape and no output tape for simplicity. Similarly for NSPACE(s(n)), TM can only use c∙s(n) nonblank tape locations, regardless of its nondeterministic choices Space bounded turing machines Read-only “input” tape. -
Three-Dimensional Bin-Packing Approaches, Issues, and Solutions
Three Dimensional Bin-Packing Issues and Solutions Seth Sweep University of Minnesota, Morris [email protected] Abstract This paper reviews the three-dimensional version of the classic NP-hard bin-packing, optimization problem along with its theoretical relevance and practical importance. Also, new approximation solutions are introduced based on prior research. The three-dimensional bin-packing optimization problem concerns placing box-shaped objects of arbitrary size and number into a box-shaped, three-dimensional space efficiently. Approximation algorithms become a guide that attempts to place objects in the least amount of space and time. As a NP-hard problem, three-dimensional bin- packing holds much academic interest to computer scientists. However, this problem also has relevance in industrial settings such as shipping cargo and efficiently designing machinery with replaceable parts, such as automobiles. Industry relies on algorithms to provide good solutions to versions of this problem. This research project adapted common approaches to one-dimensional bin-packing, such as next fit and best fit, to three dimensions. Adaptation of these algorithms is possible by dividing the space of a bin. Then, by paying attention strictly to the width of each object, the one-dimensional approaches attempt to efficiently pack into the divided compartments of the original bin. Through focusing entirely on width, these divisions effectively become multiple one-dimensional bins. However, entirely disregarding the volume of the bin by ignoring height and depth may generate inefficient packings. As this paper details in more depth, generally cubical objects may pack well but exceptionally high or long objects leave large gaps of empty space unpacked. -
Interactive Proofs for Quantum Computations
Innovations in Computer Science 2010 Interactive Proofs For Quantum Computations Dorit Aharonov Michael Ben-Or Elad Eban School of Computer Science, The Hebrew University of Jerusalem, Israel [email protected] [email protected] [email protected] Abstract: The widely held belief that BQP strictly contains BPP raises fundamental questions: Upcoming generations of quantum computers might already be too large to be simulated classically. Is it possible to experimentally test that these systems perform as they should, if we cannot efficiently compute predictions for their behavior? Vazirani has asked [21]: If computing predictions for Quantum Mechanics requires exponential resources, is Quantum Mechanics a falsifiable theory? In cryptographic settings, an untrusted future company wants to sell a quantum computer or perform a delegated quantum computation. Can the customer be convinced of correctness without the ability to compare results to predictions? To provide answers to these questions, we define Quantum Prover Interactive Proofs (QPIP). Whereas in standard Interactive Proofs [13] the prover is computationally unbounded, here our prover is in BQP, representing a quantum computer. The verifier models our current computational capabilities: it is a BPP machine, with access to few qubits. Our main theorem can be roughly stated as: ”Any language in BQP has a QPIP, and moreover, a fault tolerant one” (providing a partial answer to a challenge posted in [1]). We provide two proofs. The simpler one uses a new (possibly of independent interest) quantum authentication scheme (QAS) based on random Clifford elements. This QPIP however, is not fault tolerant. Our second protocol uses polynomial codes QAS due to Ben-Or, Cr´epeau, Gottesman, Hassidim, and Smith [8], combined with quantum fault tolerance and secure multiparty quantum computation techniques. -
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).