Complexity Problems in Enumerative Combinatorics
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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 . -
Algorithms and Computational Complexity: an Overview
Algorithms and Computational Complexity: an Overview Winter 2011 Larry Ruzzo Thanks to Paul Beame, James Lee, Kevin Wayne for some slides 1 goals Design of Algorithms – a taste design methods common or important types of problems analysis of algorithms - efficiency 2 goals Complexity & intractability – a taste solving problems in principle is not enough algorithms must be efficient some problems have no efficient solution NP-complete problems important & useful class of problems whose solutions (seemingly) cannot be found efficiently 3 complexity example Cryptography (e.g. RSA, SSL in browsers) Secret: p,q prime, say 512 bits each Public: n which equals p x q, 1024 bits In principle there is an algorithm that given n will find p and q:" try all 2512 possible p’s, but an astronomical number In practice no fast algorithm known for this problem (on non-quantum computers) security of RSA depends on this fact (and research in “quantum computing” is strongly driven by the possibility of changing this) 4 algorithms versus machines Moore’s Law and the exponential improvements in hardware... Ex: sparse linear equations over 25 years 10 orders of magnitude improvement! 5 algorithms or hardware? 107 25 years G.E. / CDC 3600 progress solving CDC 6600 G.E. = Gaussian Elimination 106 sparse linear CDC 7600 Cray 1 systems 105 Cray 2 Hardware " 104 alone: 4 orders Seconds Cray 3 (Est.) 103 of magnitude 102 101 Source: Sandia, via M. Schultz! 100 1960 1970 1980 1990 6 2000 algorithms or hardware? 107 25 years G.E. / CDC 3600 CDC 6600 G.E. = Gaussian Elimination progress solving SOR = Successive OverRelaxation 106 CG = Conjugate Gradient sparse linear CDC 7600 Cray 1 systems 105 Cray 2 Hardware " 104 alone: 4 orders Seconds Cray 3 (Est.) 103 of magnitude Sparse G.E. -
A Logical Approach to Asymptotic Combinatorics I. First Order Properties
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector ADVANCES IN MATI-EMATlCS 65, 65-96 (1987) A Logical Approach to Asymptotic Combinatorics I. First Order Properties KEVIN J. COMPTON ’ Wesleyan University, Middletown, Connecticut 06457 INTRODUCTION We shall present a general framework for dealing with an extensive set of problems from asymptotic combinatorics; this framework provides methods for determining the probability that a large, finite structure, ran- domly chosen from a given class, will have a given property. Our main concern is the asymptotic probability: the limiting value as the size of the structure increases. For example, a common problem in elementary probability texts is to show that the asymptotic probabity that a per- mutation will have no fixed point is l/e. We shall say nothing about the closely related problem of determining rates of convergence, although the methods presented here may extend to such problems. To develop a general approach we must fix a language for specifying properties of structures. Thus, our approach is logical; logic is the branch of mathematics that deals with problems of language. In this paper we con- sider properties expressible in the language of first order logic and speak of probabilities of first order sentences rather than properties. In the sequel to this paper we consider properties expressible in the more general language of monadic second order logic. Clearly, we must restrict the classes of structures we consider in order for questions about asymptotic probabilities to be meaningful and significant. Therefore, we choose to consider only classes closed under disjoint unions and components (see Sect. -
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. -
Interactions of Computational Complexity Theory and Mathematics
Interactions of Computational Complexity Theory and Mathematics Avi Wigderson October 22, 2017 Abstract [This paper is a (self contained) chapter in a new book on computational complexity theory, called Mathematics and Computation, whose draft is available at https://www.math.ias.edu/avi/book]. We survey some concrete interaction areas between computational complexity theory and different fields of mathematics. We hope to demonstrate here that hardly any area of modern mathematics is untouched by the computational connection (which in some cases is completely natural and in others may seem quite surprising). In my view, the breadth, depth, beauty and novelty of these connections is inspiring, and speaks to a great potential of future interactions (which indeed, are quickly expanding). We aim for variety. We give short, simple descriptions (without proofs or much technical detail) of ideas, motivations, results and connections; this will hopefully entice the reader to dig deeper. Each vignette focuses only on a single topic within a large mathematical filed. We cover the following: • Number Theory: Primality testing • Combinatorial Geometry: Point-line incidences • Operator Theory: The Kadison-Singer problem • Metric Geometry: Distortion of embeddings • Group Theory: Generation and random generation • Statistical Physics: Monte-Carlo Markov chains • Analysis and Probability: Noise stability • Lattice Theory: Short vectors • Invariant Theory: Actions on matrix tuples 1 1 introduction The Theory of Computation (ToC) lays out the mathematical foundations of computer science. I am often asked if ToC is a branch of Mathematics, or of Computer Science. The answer is easy: it is clearly both (and in fact, much more). Ever since Turing's 1936 definition of the Turing machine, we have had a formal mathematical model of computation that enables the rigorous mathematical study of computational tasks, algorithms to solve them, and the resources these require. -
Computational Complexity: a Modern Approach
i Computational Complexity: A Modern Approach Sanjeev Arora and Boaz Barak Princeton University http://www.cs.princeton.edu/theory/complexity/ [email protected] Not to be reproduced or distributed without the authors’ permission ii Chapter 10 Quantum Computation “Turning to quantum mechanics.... secret, secret, close the doors! we always have had a great deal of difficulty in understanding the world view that quantum mechanics represents ... It has not yet become obvious to me that there’s no real problem. I cannot define the real problem, therefore I suspect there’s no real problem, but I’m not sure there’s no real problem. So that’s why I like to investigate things.” Richard Feynman, 1964 “The only difference between a probabilistic classical world and the equations of the quantum world is that somehow or other it appears as if the probabilities would have to go negative..” Richard Feynman, in “Simulating physics with computers,” 1982 Quantum computing is a new computational model that may be physically realizable and may provide an exponential advantage over “classical” computational models such as prob- abilistic and deterministic Turing machines. In this chapter we survey the basic principles of quantum computation and some of the important algorithms in this model. One important reason to study quantum computers is that they pose a serious challenge to the strong Church-Turing thesis (see Section 1.6.3), which stipulates that every physi- cally reasonable computation device can be simulated by a Turing machine with at most polynomial slowdown. As we will see in Section 10.6, there is a polynomial-time algorithm for quantum computers to factor integers, whereas despite much effort, no such algorithm is known for deterministic or probabilistic Turing machines. -
Middleware and Application Management Architecture
Middleware and Application Management Architecture vorgelegt vom Diplom-Informatiker Sven van der Meer aus Berlin von der Fakultät IV – Elektrotechnik und Informatik der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften - Dr.-Ing. - genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Gorlatch Berichter: Prof. Dr. Dr. h.c. Radu Popescu-Zeletin Berichter: Prof. Dr. Kurt Geihs Tag der wissenschaftlichen Aussprache: 25.09.2002 Berlin 2002 D 83 Middleware and Application Management Architecture Sven van der Meer Berlin 2002 Preface Abstract This thesis describes a new approach for the integrated management of distributed networks, services, and applications. The main objective of this approach is the realization of software, systems, and services that address composability, scalability, reliability, and robustness as well as autonomous self-adaptation. It focuses on middleware for management, control, and use of fully distributed resources. The term integra- tion refers to the task of unifying existing instrumentations for middleware and management, not their replacement. The rationale of this work stems from the fact, that the current situation of middleware and management systems can be described with the term interworking. The actually needed integration of management and middleware concepts is still an open issue. However, identified trends in communications and computing demand for integrated concepts rather than the definition of new interworking scenarios. Distributed applications need to be prepared to be used and operated in a stable, secure, and efficient way. Middleware and service platforms are employed to solve this task. To guarantee this objective for a long-time operation, the systems needs to be controlled, administered, and maintained in its entirety, supporting the general aim of the system, and for each individual component, to ensure that each part of the system functions perfectly. -
An Introduction to Ramsey Theory Fast Functions, Infinity, and Metamathematics
STUDENT MATHEMATICAL LIBRARY Volume 87 An Introduction to Ramsey Theory Fast Functions, Infinity, and Metamathematics Matthew Katz Jan Reimann Mathematics Advanced Study Semesters 10.1090/stml/087 An Introduction to Ramsey Theory STUDENT MATHEMATICAL LIBRARY Volume 87 An Introduction to Ramsey Theory Fast Functions, Infinity, and Metamathematics Matthew Katz Jan Reimann Mathematics Advanced Study Semesters Editorial Board Satyan L. Devadoss John Stillwell (Chair) Rosa Orellana Serge Tabachnikov 2010 Mathematics Subject Classification. Primary 05D10, 03-01, 03E10, 03B10, 03B25, 03D20, 03H15. Jan Reimann was partially supported by NSF Grant DMS-1201263. For additional information and updates on this book, visit www.ams.org/bookpages/stml-87 Library of Congress Cataloging-in-Publication Data Names: Katz, Matthew, 1986– author. | Reimann, Jan, 1971– author. | Pennsylvania State University. Mathematics Advanced Study Semesters. Title: An introduction to Ramsey theory: Fast functions, infinity, and metamathemat- ics / Matthew Katz, Jan Reimann. Description: Providence, Rhode Island: American Mathematical Society, [2018] | Series: Student mathematical library; 87 | “Mathematics Advanced Study Semesters.” | Includes bibliographical references and index. Identifiers: LCCN 2018024651 | ISBN 9781470442903 (alk. paper) Subjects: LCSH: Ramsey theory. | Combinatorial analysis. | AMS: Combinatorics – Extremal combinatorics – Ramsey theory. msc | Mathematical logic and foundations – Instructional exposition (textbooks, tutorial papers, etc.). msc | Mathematical -
Applications and Combinatorics in Algebraic Geometry Frank Sottile Summary
Applications and Combinatorics in Algebraic Geometry Frank Sottile Summary Algebraic Geometry is a deep and well-established field within pure mathematics that is increasingly finding applications outside of mathematics. These applications in turn are the source of new questions and challenges for the subject. Many applications flow from and contribute to the more combinatorial and computational parts of algebraic geometry, and this often involves real-number or positivity questions. The scientific development of this area devoted to applications of algebraic geometry is facilitated by the sociological development of administrative structures and meetings, and by the development of human resources through the training and education of younger researchers. One goal of this project is to deepen the dialog between algebraic geometry and its appli- cations. This will be accomplished by supporting the research of Sottile in applications of algebraic geometry and in its application-friendly areas of combinatorial and computational algebraic geometry. It will be accomplished in a completely different way by supporting Sottile’s activities as an officer within SIAM and as an organizer of scientific meetings. Yet a third way to accomplish this goal will be through Sottile’s training and mentoring of graduate students, postdocs, and junior collaborators. The intellectual merits of this project include the development of applications of al- gebraic geometry and of combinatorial and combinatorial aspects of algebraic geometry. Specifically, Sottile will work to develop the theory and properties of orbitopes from the perspective of convex algebraic geometry, continue to investigate linear precision in geo- metric modeling, and apply the quantum Schubert calculus to linear systems theory. -
Combinatorics and Graph Theory I (Math 688)
Combinatorics and Graph Theory I (Math 688). Problems and Solutions. May 17, 2006 PREFACE Most of the problems in this document are the problems suggested as home- work in a graduate course Combinatorics and Graph Theory I (Math 688) taught by me at the University of Delaware in Fall, 2000. Later I added several more problems and solutions. Most of the solutions were prepared by me, but some are based on the ones given by students from the class, and from subsequent classes. I tried to acknowledge all those, and I apologize in advance if I missed someone. The course focused on Enumeration and Matching Theory. Many problems related to enumeration are taken from a 1984-85 course given by Herbert S. Wilf at the University of Pennsylvania. I would like to thank all students who took the course. Their friendly criti- cism and questions motivated some of the problems. I am especially thankful to Frank Fiedler and David Kravitz. To Frank – for sharing with me LaTeX files of his solutions and allowing me to use them. I did it several times. To David – for the technical help with the preparation of this version. Hopefully all solutions included in this document are correct, but the whole set is by no means polished. I will appreciate all comments. Please send them to [email protected]. – Felix Lazebnik 1 Problem 1. In how many 4–digit numbers abcd (a, b, c, d are the digits, a 6= 0) (i) a < b < c < d? (ii) a > b > c > d? Solution. (i) Notice that there exists a bijection between the set of our numbers and the set of all 4–subsets of the set {1, 2,..., 9}. -
CS221: Algorithms and Data Structures Lecture #4 Sorting
Today’s Outline CS221: Algorithms and • Categorizing/Comparing Sorting Algorithms Data Structures – PQSorts as examples Lecture #4 • MergeSort Sorting Things Out • QuickSort • More Comparisons Steve Wolfman • Complexity of Sorting 2014W1 1 2 Comparing our “PQSort” Categorizing Sorting Algorithms Algorithms • Computational complexity • Computational complexity – Average case behaviour: Why do we care? – Selection Sort: Always makes n passes with a – Worst/best case behaviour: Why do we care? How “triangular” shape. Best/worst/average case (n2) often do we re-sort sorted, reverse sorted, or “almost” – Insertion Sort: Always makes n passes, but if we’re sorted (k swaps from sorted where k << n) lists? lucky and search for the maximum from the right, only • Stability: What happens to elements with identical keys? constant work is needed on each pass. Best case (n); worst/average case: (n2) How much extra memory is used? • Memory Usage: – Heap Sort: Always makes n passes needing O(lg n) on each pass. Best/worst/average case: (n lg n). Note: best cases assume distinct elements. 3 With identical elements, Heap Sort can get (n) performance.4 Comparing our “PQSort” Insertion Sort Best Case Algorithms 0 1 2 3 4 5 6 7 8 9 10 11 12 13 • Stability 1 2 3 4 5 6 7 8 9 10 11 12 13 14 – Selection: Easily made stable (when building from the PQ left, prefer the left-most of identical “biggest” keys). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 – Insertion: Easily made stable (when building from the left, find the rightmost slot for a new element). -
Quantum Computational Complexity Theory Is to Un- Derstand the Implications of Quantum Physics to Computational Complexity Theory
Quantum Computational Complexity John Watrous Institute for Quantum Computing and School of Computer Science University of Waterloo, Waterloo, Ontario, Canada. Article outline I. Definition of the subject and its importance II. Introduction III. The quantum circuit model IV. Polynomial-time quantum computations V. Quantum proofs VI. Quantum interactive proof systems VII. Other selected notions in quantum complexity VIII. Future directions IX. References Glossary Quantum circuit. A quantum circuit is an acyclic network of quantum gates connected by wires: the gates represent quantum operations and the wires represent the qubits on which these operations are performed. The quantum circuit model is the most commonly studied model of quantum computation. Quantum complexity class. A quantum complexity class is a collection of computational problems that are solvable by a cho- sen quantum computational model that obeys certain resource constraints. For example, BQP is the quantum complexity class of all decision problems that can be solved in polynomial time by a arXiv:0804.3401v1 [quant-ph] 21 Apr 2008 quantum computer. Quantum proof. A quantum proof is a quantum state that plays the role of a witness or certificate to a quan- tum computer that runs a verification procedure. The quantum complexity class QMA is defined by this notion: it includes all decision problems whose yes-instances are efficiently verifiable by means of quantum proofs. Quantum interactive proof system. A quantum interactive proof system is an interaction between a verifier and one or more provers, involving the processing and exchange of quantum information, whereby the provers attempt to convince the verifier of the answer to some computational problem.