On the Polynomial Parity Argument Complexity of the Combinatorial
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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. -
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/. -
The Classes FNP and TFNP
Outline The classes FNP and TFNP C. Wilson1 1Lane Department of Computer Science and Electrical Engineering West Virginia University Christopher Wilson Function Problems Outline Outline 1 Function Problems defined What are Function Problems? FSAT Defined TSP Defined 2 Relationship between Function and Decision Problems RL Defined Reductions between Function Problems 3 Total Functions Defined Total Functions Defined FACTORING HAPPYNET ANOTHER HAMILTON CYCLE Christopher Wilson Function Problems Outline Outline 1 Function Problems defined What are Function Problems? FSAT Defined TSP Defined 2 Relationship between Function and Decision Problems RL Defined Reductions between Function Problems 3 Total Functions Defined Total Functions Defined FACTORING HAPPYNET ANOTHER HAMILTON CYCLE Christopher Wilson Function Problems Outline Outline 1 Function Problems defined What are Function Problems? FSAT Defined TSP Defined 2 Relationship between Function and Decision Problems RL Defined Reductions between Function Problems 3 Total Functions Defined Total Functions Defined FACTORING HAPPYNET ANOTHER HAMILTON CYCLE Christopher Wilson Function Problems Function Problems What are Function Problems? Function Problems FSAT Defined Total Functions TSP Defined Outline 1 Function Problems defined What are Function Problems? FSAT Defined TSP Defined 2 Relationship between Function and Decision Problems RL Defined Reductions between Function Problems 3 Total Functions Defined Total Functions Defined FACTORING HAPPYNET ANOTHER HAMILTON CYCLE Christopher Wilson Function Problems Function -
Lecture: Complexity of Finding a Nash Equilibrium 1 Computational
Algorithmic Game Theory Lecture Date: September 20, 2011 Lecture: Complexity of Finding a Nash Equilibrium Lecturer: Christos Papadimitriou Scribe: Miklos Racz, Yan Yang In this lecture we are going to talk about the complexity of finding a Nash Equilibrium. In particular, the class of problems NP is introduced and several important subclasses are identified. Most importantly, we are going to prove that finding a Nash equilibrium is PPAD-complete (defined in Section 2). For an expository article on the topic, see [4], and for a more detailed account, see [5]. 1 Computational complexity For us the best way to think about computational complexity is that it is about search problems.Thetwo most important classes of search problems are P and NP. 1.1 The complexity class NP NP stands for non-deterministic polynomial. It is a class of problems that are at the core of complexity theory. The classical definition is in terms of yes-no problems; here, we are concerned with the search problem form of the definition. Definition 1 (Complexity class NP). The class of all search problems. A search problem A is a binary predicate A(x, y) that is efficiently (in polynomial time) computable and balanced (the length of x and y do not differ exponentially). Intuitively, x is an instance of the problem and y is a solution. The search problem for A is this: “Given x,findy such that A(x, y), or if no such y exists, say “no”.” The class of all search problems is called NP. Examples of NP problems include the following two. -
The Computational Complexity Column by V
The Computational Complexity Column by V. Arvind Institute of Mathematical Sciences, CIT Campus, Taramani Chennai 600113, India [email protected] http://www.imsc.res.in/~arvind The mid-1960’s to the early 1980’s marked the early epoch in the field of com- putational complexity. Several ideas and research directions which shaped the field at that time originated in recursive function theory. Some of these made a lasting impact and still have interesting open questions. The notion of lowness for complexity classes is a prominent example. Uwe Schöning was the first to study lowness for complexity classes and proved some striking results. The present article by Johannes Köbler and Jacobo Torán, written on the occasion of Uwe Schöning’s soon-to-come 60th birthday, is a nice introductory survey on this intriguing topic. It explains how lowness gives a unifying perspective on many complexity results, especially about problems that are of intermediate complexity. The survey also points to the several questions that still remain open. Lowness results: the next generation Johannes Köbler Humboldt Universität zu Berlin [email protected] Jacobo Torán Universität Ulm [email protected] Abstract Our colleague and friend Uwe Schöning, who has helped to shape the area of Complexity Theory in many decisive ways is turning 60 this year. As a little birthday present we survey in this column some of the newer results related with the concept of lowness, an idea that Uwe translated from the area of Recursion Theory in the early eighties. Originally this concept was applied to the complexity classes in the polynomial time hierarchy. -
The Relative Complexity of NP Search Problems
Journal of Computer and System Sciences 57, 319 (1998) Article No. SS981575 The Relative Complexity of NP Search Problems Paul Beame* Computer Science and Engineering, University of Washington, Box 352350, Seattle, Washington 98195-2350 E-mail: beameÄcs.washington.edu Stephen Cook- Computer Science Department, University of Toronto, Canada M5S 3G4 E-mail: sacookÄcs.toronto.edu Jeff Edmonds Department of Computer Science, York University, Toronto, Ontario, Canada M3J 1P3 E-mail: jeffÄcs.yorku.ca Russell Impagliazzo9 Computer Science and Engineering, UC, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0114 E-mail: russellÄcs.ucsd.edu and Toniann PitassiÄ Department of Computer Science, University of Arizona, Tucson, Arizona 85721-0077 E-mail: toniÄcs.arizona.edu Received January 1998 1. INTRODUCTION Papadimitriou introduced several classes of NP search problems based on combinatorial principles which guarantee the existence of In the study of computational complexity, there are many solutions to the problems. Many interesting search problems not known to be solvable in polynomial time are contained in these classes, problems that are naturally expressed as problems ``to find'' and a number of them are complete problems. We consider the question but are converted into decision problems to fit into standard of the relative complexity of these search problem classes. We prove complexity classes. For example, a more natural problem several separations which show that in a generic relativized world the than determining whether or not a graph is 3-colorable search classes are distinct and there is a standard search problem in might be that of finding a 3-coloring of the graph if it exists. -
Physics 212B, Ken Intriligator Lecture 6, Jan 29, 2018 • Parity P Acts As P
Physics 212b, Ken Intriligator lecture 6, Jan 29, 2018 Parity P acts as P = P † = P −1 with P~vP = ~v and P~aP = ~a for vectors (e.g. ~x • − and ~p) and axial pseudo-vectors (e.g. L~ and B~ ) respectively. Note that it is not a rotation. Parity preserves all the basic commutation relations, but it is not necessarily a symmetry of the Hamiltonian. The names scalar and pseudo scalar are often used to denote parity even vs odd: P P = for “scalar” operators (like ~x2 or L~ 2, and P ′P = ′ for pseudo O O O −O scalar operators (like S~ ~x). · Nature respects parity aside from some tiny effects at the fundamental level. E.g. • in electricity and magnetism, we can preserve Maxwell’s equations via V~ ~ V and → − V~ +V~ . axial → axial In QM, parity is represented by a unitary operator such that P ~r = ~r and • | i | − i P 2 = 1. So the eigenvectors of P have eigenvalue 1. A state is even or odd under parity ± if P ψ = ψ ; in position space, ψ( ~x) = ψ(~x). In radial coordinates, parity takes | i ±| i − ± cos θ cos θ and φ φ + π, so P α,ℓ,m =( 1)ℓ α,ℓ,m . This is clear also from the → − → | i − | i ℓ = 1 case, any getting general ℓ from tensor products. If [H,P ] = 0, then the non-degenerate energy eigenstates can be written as P eigen- states. Parity in 1d: PxP = x and P pP = p. The Hamiltonian respects parity if V ( x) = − − − V (x). -
Constructive Galois Connections
ZU064-05-FPR main 23 July 2018 12:36 1 Constructive Galois Connections DAVID DARAIS University of Vermont, USA (e-mail: [email protected]) DAVID VAN HORN University of Maryland, College Park, USA (e-mail: [email protected]) Abstract Galois connections are a foundational tool for structuring abstraction in semantics and their use lies at the heart of the theory of abstract interpretation. Yet, mechanization of Galois connections using proof assistants remains limited to restricted modes of use, preventing their general application in mechanized metatheory and certified programming. This paper presents constructive Galois connections, a variant of Galois connections that is effective both on paper and in proof assistants; is complete with respect toa large subset of classical Galois connections; and enables more general reasoning principles, including the “calculational” style advocated by Cousot. To design constructive Galois connections we identify a restricted mode of use of classical ones which is both general and amenable to mechanization in dependently-typed functional programming languages. Crucial to our metatheory is the addition of monadic structure to Galois connections to control a “specification effect.” Effectful calculations may reason classically, while pure calculations have extractable computational content. Explicitly moving between the worlds of specification and implementation is enabled by our metatheory. To validate our approach, we provide two case studies in mechanizing existing proofs from the literature: the first uses calculational abstract interpretation to design a static analyzer; the second forms a semantic basis for gradual typing. Both mechanized proofs closely follow their original paper-and-pencil counterparts, employ reasoning principles arXiv:1807.08711v1 [cs.PL] 23 Jul 2018 not captured by previous mechanization approaches, support the extraction of verified algorithms, and are novel. -
Computational Complexity; Slides 9, HT 2019 NP Search Problems, and Total Search Problems
Computational Complexity; slides 9, HT 2019 NP search problems, and total search problems Prof. Paul W. Goldberg (Dept. of Computer Science, University of Oxford) HT 2019 Paul Goldberg NP search problems, and total search problems 1 / 21 Examples of total search problems in NP FACTORING NASH: the problem of computing a Nash equilibrium of a game (comes in many versions depending on the structure of the game) PIGEONHOLE CIRCUIT: Input: a boolean circuit with n input gates and n output gates Output: either input vector x mapping to 0 or vectors x, x0 mapping to the same output NECKLACE SPLITTING SECOND HAMILTONIAN CYCLE (in 3-regular graph) HAM SANDWICH: search for ham sandwich cut Search for local optima in settings with neighbourhood structure The above seem to be hard. (of course, many search probs are in P, e.g. input a list L of numbers, output L in increasing order) Paul Goldberg NP search problems, and total search problems 2 / 21 Search problems as poly-time checkable relations NP search problem is modelled as a relation R(·; ·) where R(x; y) is checkable in time polynomial in jxj, jyj input x, find y with R(x; y)( y as certificate) total search problem: 8x9y (jyj = poly(jxj); R(x; y)) SAT: x is boolean formula, y is satisfying bit vector. Decision version of SAT is polynomial-time equivalent to search for y. FACTORING: input (the \x" in R(x; y)) is number N, output (the \y") is prime factorisation of N. No decision problem! NECKLACE SPLITTING (k thieves): input is string of n beads in c colours; output is a decomposition into c(k − 1) + 1 substrings and allocation of substrings to thieves such that they all get the same number of beads of each colour. -
On Total Functions, Existence Theorems and Computational Complexity
Theoretical Computer Science 81 (1991) 317-324 Elsevier Note On total functions, existence theorems and computational complexity Nimrod Megiddo IBM Almaden Research Center, 650 Harry Road, Sun Jose, CA 95120-6099, USA, and School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel Christos H. Papadimitriou* Computer Technology Institute, Patras, Greece, and University of California at Sun Diego, CA, USA Communicated by M. Nivat Received October 1989 Abstract Megiddo, N. and C.H. Papadimitriou, On total functions, existence theorems and computational complexity (Note), Theoretical Computer Science 81 (1991) 317-324. wondeterministic multivalued functions with values that are polynomially verifiable and guaran- teed to exist form an interesting complexity class between P and NP. We show that this class, which we call TFNP, contains a host of important problems, whose membership in P is currently not known. These include, besides factoring, local optimization, Brouwer's fixed points, a computa- tional version of Sperner's Lemma, bimatrix equilibria in games, and linear complementarity for P-matrices. 1. The class TFNP Let 2 be an alphabet with two or more symbols, and suppose that R G E*x 2" is a polynomial-time recognizable relation which is polynomially balanced, that is, (x,y) E R implies that lyl sp(lx()for some polynomial p. * Research supported by an ESPRIT Basic Research Project, a grant to the Universities of Patras and Bonn by the Volkswagen Foundation, and an NSF Grant. Research partially performed while the author was visiting the IBM Almaden Research Center. 0304-3975/91/$03.50 @ 1991-Elsevier Science Publishers B.V. -
A Tour of the Complexity Classes Between P and NP
A Tour of the Complexity Classes Between P and NP John Fearnley University of Liverpool Joint work with Spencer Gordon, Ruta Mehta, Rahul Savani Simple stochastic games T A two player game I Maximizer (box) wants to reach T I Minimizer (triangle) who wants to avoid T I Nature (circle) plays uniformly at random Simple stochastic games 0.5 1 1 0.5 0.5 0 Value of a vertex: I The largest probability of winning that max can ensure I The smallest probability of winning that min can ensure Computational Problem: find the value of each vertex Simple stochastic games 0.5 1 1 0.5 0.5 0 Is the problem I Easy? Does it have a polynomial time algorithm? I Hard? Perhaps no such algorithm exists This is currently unresolved Simple stochastic games 0.5 1 1 0.5 0.5 0 The problem lies in NP \ co-NP I So it is unlikely to be NP-hard But there are a lot of NP-intermediate classes... This talk: whare are these complexity classes? Simple stochastic games Solving a simple-stochastic game lies in NP \ co-NP \ UP \ co-UP \ TFNP \ PPP \ PPA \ PPAD \ PLS \ CLS \ EOPL \ UEOPL Simple stochastic games Solving a simple-stochastic game lies in NP \ co-NP \ UP \ co-UP \ TFNP \ PPP \ PPA \ PPAD \ PLS \ CLS \ EOPL \ UEOPL This talk: whare are these complexity classes? TFNP PPAD PLS CLS Complexity classes between P and NP NP P There are many problems that lie between P and NP I Factoring, graph isomorphism, computing Nash equilibria, local max cut, simple-stochastic games, .. -
Total Functions in QMA
Total Functions in QMA Serge Massar1 and Miklos Santha2,3 1Laboratoire d’Information Quantique CP224, Université libre de Bruxelles, B-1050 Brussels, Belgium. 2CNRS, IRIF, Université Paris Diderot, 75205 Paris, France. 3Centre for Quantum Technologies & MajuLab, National University of Singapore, Singapore. December 15, 2020 The complexity class QMA is the quan- either output a circuit with length less than `, or tum analog of the classical complexity class output NO if such a circuit does not exist. The NP. The functional analogs of NP and functional analog of P, denoted FP, is the subset QMA, called functional NP (FNP) and of FNP for which the output can be computed in functional QMA (FQMA), consist in ei- polynomial time. ther outputting a (classical or quantum) Total functional NP (TFNP), introduced in witness, or outputting NO if there does [37] and which lies between FP and FNP, is the not exist a witness. The classical com- subset of FNP for which it can be shown that the plexity class Total Functional NP (TFNP) NO outcome never occurs. As an example, factor- is the subset of FNP for which it can be ing (given an integer n, output the prime factors shown that the NO outcome never occurs. of n) lies in TFNP since for all n a (unique) set TFNP includes many natural and impor- of prime factors exists, and it can be verified in tant problems. Here we introduce the polynomial time that the factorisation is correct. complexity class Total Functional QMA TFNP can also be defined as the functional ana- (TFQMA), the quantum analog of TFNP.