Recent Advances in Complexity Theory
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Notes for Lecture 2
Notes on Complexity Theory Last updated: December, 2011 Lecture 2 Jonathan Katz 1 Review The running time of a Turing machine M on input x is the number of \steps" M takes before it halts. Machine M is said to run in time T (¢) if for every input x the running time of M(x) is at most T (jxj). (In particular, this means it halts on all inputs.) The space used by M on input x is the number of cells written to by M on all its work tapes1 (a cell that is written to multiple times is only counted once); M is said to use space T (¢) if for every input x the space used during the computation of M(x) is at most T (jxj). We remark that these time and space measures are worst-case notions; i.e., even if M runs in time T (n) for only a fraction of the inputs of length n (and uses less time for all other inputs of length n), the running time of M is still T . (Average-case notions of complexity have also been considered, but are somewhat more di±cult to reason about. We may cover this later in the semester; or see [1, Chap. 18].) Recall that a Turing machine M computes a function f : f0; 1g¤ ! f0; 1g¤ if M(x) = f(x) for all x. We will focus most of our attention on boolean functions, a context in which it is more convenient to phrase computation in terms of languages. A language is simply a subset of f0; 1g¤. -
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. -
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. -
Dspace 6.X Documentation
DSpace 6.x Documentation DSpace 6.x Documentation Author: The DSpace Developer Team Date: 27 June 2018 URL: https://wiki.duraspace.org/display/DSDOC6x Page 1 of 924 DSpace 6.x Documentation Table of Contents 1 Introduction ___________________________________________________________________________ 7 1.1 Release Notes ____________________________________________________________________ 8 1.1.1 6.3 Release Notes ___________________________________________________________ 8 1.1.2 6.2 Release Notes __________________________________________________________ 11 1.1.3 6.1 Release Notes _________________________________________________________ 12 1.1.4 6.0 Release Notes __________________________________________________________ 14 1.2 Functional Overview _______________________________________________________________ 22 1.2.1 Online access to your digital assets ____________________________________________ 23 1.2.2 Metadata Management ______________________________________________________ 25 1.2.3 Licensing _________________________________________________________________ 27 1.2.4 Persistent URLs and Identifiers _______________________________________________ 28 1.2.5 Getting content into DSpace __________________________________________________ 30 1.2.6 Getting content out of DSpace ________________________________________________ 33 1.2.7 User Management __________________________________________________________ 35 1.2.8 Access Control ____________________________________________________________ 36 1.2.9 Usage Metrics _____________________________________________________________ -
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/. -
Automating Cutting Planes Is NP-Hard
Automating Cutting Planes is NP-Hard Mika G¨o¨os† Sajin Koroth Ian Mertz Toniann Pitassi Stanford University Simon Fraser University Uni. of Toronto Uni. of Toronto & IAS April 20, 2020 Abstract We show that Cutting Planes (CP) proofs are hard to find: Given an unsatisfiable formula F , (1) it is NP-hard to find a CP refutation of F in time polynomial in the length of the shortest such refutation; and (2) unless Gap-Hitting-Set admits a nontrivial algorithm, one cannot find a tree-like CP refutation of F in time polynomial in the length of the shortest such refutation. The first result extends the recent breakthrough of Atserias and M¨uller (FOCS 2019) that established an analogous result for Resolution. Our proofs rely on two new lifting theorems: (1) Dag-like lifting for gadgets with many output bits. (2) Tree-like lifting that simulates an r-round protocol with gadgets of query complexity O(log r) independent of input length. Contents 1 Introduction1 4.1 Subcubes from simplices . .9 1.1 Cutting Planes . .1 4.2 Simplified proof . 11 1.2 Dag-like result . .2 4.3 Accounting for error . 12 1.3 Tree-like result . .2 5 Tree-like definitions 13 2 Overview of proofs3 5.1 Tree-like dags/proofs . 13 2.1 Dag-like case . .3 5.2 Real protocols . 14 arXiv:2004.08037v1 [cs.CC] 17 Apr 2020 2.2 Tree-like case . .4 6 Tree-like lifting 14 3 Dag-like definitions6 6.1 Proof of real lifting . 14 3.1 Standard models . -
When Subgraph Isomorphism Is Really Hard, and Why This Matters for Graph Databases Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble
When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble To cite this version: Ciaran Mccreesh, Patrick Prosser, Christine Solnon, James Trimble. When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases. Journal of Artificial Intelligence Research, Association for the Advancement of Artificial Intelligence, 2018, 61, pp.723 - 759. 10.1613/jair.5768. hal-01741928 HAL Id: hal-01741928 https://hal.archives-ouvertes.fr/hal-01741928 Submitted on 26 Mar 2018 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. Journal of Artificial Intelligence Research 61 (2018) 723-759 Submitted 11/17; published 03/18 When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases Ciaran McCreesh [email protected] Patrick Prosser [email protected] University of Glasgow, Glasgow, Scotland Christine Solnon [email protected] INSA-Lyon, LIRIS, UMR5205, F-69621, France James Trimble [email protected] University of Glasgow, Glasgow, Scotland Abstract The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. -
Glossary of Complexity Classes
App endix A Glossary of Complexity Classes Summary This glossary includes selfcontained denitions of most complexity classes mentioned in the b o ok Needless to say the glossary oers a very minimal discussion of these classes and the reader is re ferred to the main text for further discussion The items are organized by topics rather than by alphab etic order Sp ecically the glossary is partitioned into two parts dealing separately with complexity classes that are dened in terms of algorithms and their resources ie time and space complexity of Turing machines and complexity classes de ned in terms of nonuniform circuits and referring to their size and depth The algorithmic classes include timecomplexity based classes such as P NP coNP BPP RP coRP PH E EXP and NEXP and the space complexity classes L NL RL and P S P AC E The non k uniform classes include the circuit classes P p oly as well as NC and k AC Denitions and basic results regarding many other complexity classes are available at the constantly evolving Complexity Zoo A Preliminaries Complexity classes are sets of computational problems where each class contains problems that can b e solved with sp ecic computational resources To dene a complexity class one sp ecies a mo del of computation a complexity measure like time or space which is always measured as a function of the input length and a b ound on the complexity of problems in the class We follow the tradition of fo cusing on decision problems but refer to these problems using the terminology of promise problems -
A Short History of Computational Complexity
The Computational Complexity Column by Lance FORTNOW NEC Laboratories America 4 Independence Way, Princeton, NJ 08540, USA [email protected] http://www.neci.nj.nec.com/homepages/fortnow/beatcs Every third year the Conference on Computational Complexity is held in Europe and this summer the University of Aarhus (Denmark) will host the meeting July 7-10. More details at the conference web page http://www.computationalcomplexity.org This month we present a historical view of computational complexity written by Steve Homer and myself. This is a preliminary version of a chapter to be included in an upcoming North-Holland Handbook of the History of Mathematical Logic edited by Dirk van Dalen, John Dawson and Aki Kanamori. A Short History of Computational Complexity Lance Fortnow1 Steve Homer2 NEC Research Institute Computer Science Department 4 Independence Way Boston University Princeton, NJ 08540 111 Cummington Street Boston, MA 02215 1 Introduction It all started with a machine. In 1936, Turing developed his theoretical com- putational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40's and 50's, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960's by Hartmanis and Stearns. -
Group, Graphs, Algorithms: the Graph Isomorphism Problem
Proc. Int. Cong. of Math. – 2018 Rio de Janeiro, Vol. 3 (3303–3320) GROUP, GRAPHS, ALGORITHMS: THE GRAPH ISOMORPHISM PROBLEM László Babai Abstract Graph Isomorphism (GI) is one of a small number of natural algorithmic problems with unsettled complexity status in the P / NP theory: not expected to be NP-complete, yet not known to be solvable in polynomial time. Arguably, the GI problem boils down to filling the gap between symmetry and regularity, the former being defined in terms of automorphisms, the latter in terms of equations satisfied by numerical parameters. Recent progress on the complexity of GI relies on a combination of the asymptotic theory of permutation groups and asymptotic properties of highly regular combinato- rial structures called coherent configurations. Group theory provides the tools to infer either global symmetry or global irregularity from local information, eliminating the symmetry/regularity gap in the relevant scenario; the resulting global structure is the subject of combinatorial analysis. These structural studies are melded in a divide- and-conquer algorithmic framework pioneered in the GI context by Eugene M. Luks (1980). 1 Introduction We shall consider finite structures only; so the terms “graph” and “group” will refer to finite graphs and groups, respectively. 1.1 Graphs, isomorphism, NP-intermediate status. A graph is a set (the set of ver- tices) endowed with an irreflexive, symmetric binary relation called adjacency. Isomor- phisms are adjacency-preseving bijections between the sets of vertices. The Graph Iso- morphism (GI) problem asks to determine whether two given graphs are isomorphic. It is known that graphs are universal among explicit finite structures in the sense that the isomorphism problem for explicit structures can be reduced in polynomial time to GI (in the sense of Karp-reductions1) Hedrlı́n and Pultr [1966] and Miller [1979]. -
Some Hardness Escalation Results in Computational Complexity Theory Pritish Kamath
Some Hardness Escalation Results in Computational Complexity Theory by Pritish Kamath B.Tech. Indian Institute of Technology Bombay (2012) S.M. Massachusetts Institute of Technology (2015) Submitted to Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering & Computer Science at Massachusetts Institute of Technology February 2020 ⃝c Massachusetts Institute of Technology 2019. All rights reserved. Author: ............................................................. Department of Electrical Engineering and Computer Science September 16, 2019 Certified by: ............................................................. Ronitt Rubinfeld Professor of Electrical Engineering and Computer Science, MIT Thesis Supervisor Certified by: ............................................................. Madhu Sudan Gordon McKay Professor of Computer Science, Harvard University Thesis Supervisor Accepted by: ............................................................. Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science, MIT Chair, Department Committee on Graduate Students Some Hardness Escalation Results in Computational Complexity Theory by Pritish Kamath Submitted to Department of Electrical Engineering and Computer Science on September 16, 2019, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science & Engineering Abstract In this thesis, we prove new hardness escalation results in computational complexity theory; a phenomenon where hardness results against seemingly weak models of computation for any problem can be lifted, in a black box manner, to much stronger models of computation by considering a simple gadget composed version of the original problem. For any unsatisfiable CNF formula F that is hard to refute in the Resolution proof system, we show that a gadget-composed version of F is hard to refute in any proof system whose lines are computed by efficient communication protocols. -
Complexity, Part 2 —
EXPTIME-membership EXPTIME-hardness PSPACE-hardness harder Description Logics: a Nice Family of Logics — Complexity, Part 2 — Uli Sattler1 Thomas Schneider 2 1School of Computer Science, University of Manchester, UK 2Department of Computer Science, University of Bremen, Germany ESSLLI, 18 August 2016 Uli Sattler, Thomas Schneider DL: Complexity (2) 1 EXPTIME-membership EXPTIME-hardness PSPACE-hardness harder Goal for today Automated reasoning plays an important role for DLs. It allows the development of intelligent applications. The expressivity of DLs is strongly tailored towards this goal. Requirements for automated reasoning: Decidability of the relevant decision problems Low complexity if possible Algorithms that perform well in practice Yesterday & today: 1 & 3 Now: 2 Uli Sattler, Thomas Schneider DL: Complexity (2) 2 EXPTIME-membership EXPTIME-hardness PSPACE-hardness harder Plan for today 1 EXPTIME-membership 2 EXPTIME-hardness 3 PSPACE-hardness (not covered in the lecture) 4 Undecidability and a NEXPTIME lower bound ; Uli Thanks to Carsten Lutz for much of the material on these slides. Uli Sattler, Thomas Schneider DL: Complexity (2) 3 EXPTIME-membership EXPTIME-hardness PSPACE-hardness harder And now . 1 EXPTIME-membership 2 EXPTIME-hardness 3 PSPACE-hardness (not covered in the lecture) 4 Undecidability and a NEXPTIME lower bound ; Uli Uli Sattler, Thomas Schneider DL: Complexity (2) 4 EXPTIME-membership EXPTIME-hardness PSPACE-hardness harder EXPTIME-membership We start with an EXPTIME upper bound for concept satisfiability in ALC relative to TBoxes. Theorem The following problem is in EXPTIME. Input: an ALC concept C0 and an ALC TBox T I Question: is there a model I j= T with C0 6= ; ? We’ll use a technique known from modal logic: type elimination [Pratt 1978] The basis is a syntactic notion of a type.