The Nature of Computation
|
|
- Percival Atkins
- 5 years ago
- Views:
Transcription
1 The Nature of Computation Cristopher Moore University of New Mexico, Albuquerque and Santa Fe Institute Stephan Mertens Otto-von-Guericke University, Magdeburg and Santa Fe Institute OXFORD UNIVERSITY PRESS
2 Contents Figure Credits Preface xv 1 Prologue Crossing Bridges Intractable Itineraries Playing Chess With God What Lies Ahead 10 Problems 11 Notes 13 2 The Basics 2.1 Problems and Solutions 2.2 Time, Space, and Scaling 2.3 Intrinsic Complexity 2.4 The Importance of Being Polynomial 2.5 Tractability and Mathematical Insight Problems Notes 3 Insights and Algorithms 3.1 Recursion 3.2 Divide and Conquer 3.3 Dynamic Programming 3.4 Getting There From Here 3.5 When Greed is Good 3.6 Finding a Better Flow 3.7 Flows, Cuts, and Duality 3.8 Transformations and Reductions Problems Notes vii
3 viii CONTENTS 4 Needles in a Haystack: the Class NP Needles and Haystacks A Tour of NP Search, Existence, and Nondeterminism Knots and Primes 115 Problems 121 Notes Who is the Hardest One of All? NP-Completeness 5.1 When One Problem Captures Them All 5.2 Circuits and Formulas 5.3 D esigning Reductions 5.4 Completeness as a Surprise 5.5 The Boundary Between Easy and Hard 5.6 Finally, Hamiltonian Path Problems Notes The Deep Question: P vs. NP What if P -=NP? Upper Bounds are Easy, Lower Bounds Are Hard Diagonalization and the Time Hierarchy Possible Worlds Natural Proofs Problems in the Gap Nonconstructive Proofs The Road Ahead 210 Problems 211 Notes The Grand Unified Theory of Computation Babbage's Vision and Hilbert's Dream Universality and Undecidability Building Blocks: Recursive Functions Form is Function: the 2.-Calculus Turing's Applied Philosophy Computation Everywhere 264 Problems 284 Notes Memory, Paths, and Garnes Welcome to the State Space Show Me The Way L and NL-Completeness 310
4 CONTENTS ix 8.4 Middle-First Search and Nondeterministic Space You Can't Get There From Here PSPACE, Garnes, and Quantified SAT Garnes People Play Symmetric Space 339 Problems 341 Notes Optimization and Approximation Three Flavors of Optimization Approximations Inapproximability Jewels and Facets: Linear Programming Through the Looking-Glass: Duality Solving by Balloon: Interior Point Methods Hunting with Eggshells Algorithmic Cubism Trees, Tours, and Polytopes Solving Hard Problems in Practice 414 Problems 427 Notes Randomized Algorithms Foiling the Adversary The Smallest Cut The Satisfied Drunkard: Wa1kSAT Solving in Heaven, Projecting to Earth Garnes Against the Adversary Fingerprints, Hash Functions, and Uniqueness The Roots of Identity Primality Randomized Complexity Classes 488 Problems 491 Notes Interaction and Pseudorandomness The Tale of Arthur and Merlin The Fable of the Chess Master Probabilistically Checkable Proofs Pseudorandom Generators and Derandomization 540 Problems 553 Notes 560
5 x CONTENTS 12 Random Walks and Rapid Mixing A Random Walk in Physics The Approach to Equilibrium Equilibrium Indicators Coupling Coloring a Graph, Randomly Burying Ancient History: Coupling from the Past The Spectral Gap Flows of Probability: Conductance Expanders Mixing in Time and Space 623 Problems 626 Notes Counting, Sampling, and Statistical Physics Spanning Trees and the Determinant Perfect Matchings and the Permanent The Complexity of Counting From Counting to Sampling, and Back Random Matchings and Approximating the Permanent Planar Graphs and Asymptotics an Lattices Solving the Ising Model 693 Problems 703 Notes When Formulas Freeze: Phase Transitions in Computation Experiments and Conjectures Random Graphs, Giant Components, and Cores Equations of Motion: Algorithmic Lower Bounds Magic Moments The Easiest Hard Problem Message Passing Survey Propagation and the Geometry of Solutions Frozen Variables and Hardness 793 Problems 796 Notes Quantum Computation Particles, Waves, and Amplitudes States and Operators Spooky Action at a Distance Algorithmic Interference Cryptography and Shor's Algorithm Graph Isomorphism and the Hidden Subgroup Problem 862
6 CONTENTS )d 15.7 Quantum Haystacks: Grover's Algorithm Quantum Walks and Scattering 876 Problems 888 Notes 902 A Mathematical Tools 911 A.1 The Story of A.2 Approximations and Inequalities 914 A.3 Chance and Necessity 917 A.4 Dice and Drunkards 923 A.5 Concentration Inequalities 927 A.6 Asymptotic Integrals 931 A.7 Groups, Rings, and Fields 933 Problems 939 References 945 Index 974
A Tale of Two Cultures: Phase Transitions in Physics and Computer Science. Cristopher Moore University of New Mexico and the Santa Fe Institute
A Tale of Two Cultures: Phase Transitions in Physics and Computer Science Cristopher Moore University of New Mexico and the Santa Fe Institute Computational Complexity Why are some problems qualitatively
More informationCOMPUTATIONAL COMPLEXITY
COMPUTATIONAL COMPLEXITY A Modern Approach SANJEEV ARORA Princeton University BOAZ BARAK Princeton University {Щ CAMBRIDGE Щ0 UNIVERSITY PRESS Contents About this book Acknowledgments Introduction page
More informationThe P versus NP Problem. Ker-I Ko. Stony Brook, New York
The P versus NP Problem Ker-I Ko Stony Brook, New York ? P = NP One of the seven Millenium Problems The youngest one A folklore question? Has hundreds of equivalent forms Informal Definitions P : Computational
More informationCSC 1700 Analysis of Algorithms: P and NP Problems
CSC 1700 Analysis of Algorithms: P and NP Problems Professor Henry Carter Fall 2016 Recap Algorithmic power is broad but limited Lower bounds determine whether an algorithm can be improved by more than
More informationPhase Transitions in Physics and Computer Science. Cristopher Moore University of New Mexico and the Santa Fe Institute
Phase Transitions in Physics and Computer Science Cristopher Moore University of New Mexico and the Santa Fe Institute Magnetism When cold enough, Iron will stay magnetized, and even magnetize spontaneously
More informationQuantum Complexity Theory. Wim van Dam HP Labs MSRI UC Berkeley SQUINT 3 June 16, 2003
Quantum Complexity Theory Wim van Dam HP Labs MSRI UC Berkeley SQUINT 3 June 16, 2003 Complexity Theory Complexity theory investigates what resources (time, space, randomness, etc.) are required to solve
More informationVIII. NP-completeness
VIII. NP-completeness 1 / 15 NP-Completeness Overview 1. Introduction 2. P and NP 3. NP-complete (NPC): formal definition 4. How to prove a problem is NPC 5. How to solve a NPC problem: approximate algorithms
More informationComputational Complexity Theory. The World of P and NP. Jin-Yi Cai Computer Sciences Dept University of Wisconsin, Madison
Computational Complexity Theory The World of P and NP Jin-Yi Cai Computer Sciences Dept University of Wisconsin, Madison Sept 11, 2012 Supported by NSF CCF-0914969. 1 2 Entscheidungsproblem The rigorous
More informationQuantum Computing Lecture 8. Quantum Automata and Complexity
Quantum Computing Lecture 8 Quantum Automata and Complexity Maris Ozols Computational models and complexity Shor s algorithm solves, in polynomial time, a problem for which no classical polynomial time
More informationCS154, Lecture 18: 1
CS154, Lecture 18: 1 CS 154 Final Exam Wednesday December 12, 12:15-3:15 pm STLC 111 You re allowed one double-sided sheet of notes Exam is comprehensive (but will emphasize post-midterm topics) Look for
More informationIntroduction to Quantum Computing
Introduction to Quantum Computing Part I Emma Strubell http://cs.umaine.edu/~ema/quantum_tutorial.pdf April 12, 2011 Overview Outline What is quantum computing? Background Caveats Fundamental differences
More informationComputational Complexity: A Modern Approach
1 Computational Complexity: A Modern Approach Draft of a book in preparation: Dated December 2004 Comments welcome! Sanjeev Arora Not to be reproduced or distributed without the author s permission I am
More informationNP Complete Problems. COMP 215 Lecture 20
NP Complete Problems COMP 215 Lecture 20 Complexity Theory Complexity theory is a research area unto itself. The central project is classifying problems as either tractable or intractable. Tractable Worst
More informationCSC 8301 Design & Analysis of Algorithms: Lower Bounds
CSC 8301 Design & Analysis of Algorithms: Lower Bounds Professor Henry Carter Fall 2016 Recap Iterative improvement algorithms take a feasible solution and iteratively improve it until optimized Simplex
More informationCOMP/MATH 300 Topics for Spring 2017 June 5, Review and Regular Languages
COMP/MATH 300 Topics for Spring 2017 June 5, 2017 Review and Regular Languages Exam I I. Introductory and review information from Chapter 0 II. Problems and Languages A. Computable problems can be expressed
More informationComputability and Complexity Theory: An Introduction
Computability and Complexity Theory: An Introduction meena@imsc.res.in http://www.imsc.res.in/ meena IMI-IISc, 20 July 2006 p. 1 Understanding Computation Kinds of questions we seek answers to: Is a given
More information1. Introduction Recap
1. Introduction Recap 1. Tractable and intractable problems polynomial-boundness: O(n k ) 2. NP-complete problems informal definition 3. Examples of P vs. NP difference may appear only slightly 4. Optimization
More informationComputational Complexity
p. 1/24 Computational Complexity The most sharp distinction in the theory of computation is between computable and noncomputable functions; that is, between possible and impossible. From the example of
More information7.11 A proof involving composition Variation in terminology... 88
Contents Preface xi 1 Math review 1 1.1 Some sets............................. 1 1.2 Pairs of reals........................... 3 1.3 Exponentials and logs...................... 4 1.4 Some handy functions......................
More informationDRAFT -- DRAFT -- DRAFT -- DRAFT -- DRAFT --
1 Computational Complexity: A Modern Approach Draft of a book: Dated February 2006 Comments welcome! Sanjeev Arora Princeton University arora@cs.princeton.edu Not to be reproduced or distributed without
More information*WILEY- Quantum Computing. Joachim Stolze and Dieter Suter. A Short Course from Theory to Experiment. WILEY-VCH Verlag GmbH & Co.
Joachim Stolze and Dieter Suter Quantum Computing A Short Course from Theory to Experiment Second, Updated and Enlarged Edition *WILEY- VCH WILEY-VCH Verlag GmbH & Co. KGaA Contents Preface XIII 1 Introduction
More informationCMSC 441: Algorithms. NP Completeness
CMSC 441: Algorithms NP Completeness Intractable & Tractable Problems Intractable problems: As they grow large, we are unable to solve them in reasonable time What constitutes reasonable time? Standard
More informationAlgorithms Design & Analysis. Approximation Algorithm
Algorithms Design & Analysis Approximation Algorithm Recap External memory model Merge sort Distribution sort 2 Today s Topics Hard problem Approximation algorithms Metric traveling salesman problem A
More informationUCSD CSE 21, Spring 2014 [Section B00] Mathematics for Algorithm and System Analysis
UCSD CSE 21, Spring 2014 [Section B00] Mathematics for Algorithm and System Analysis Final Exam Review Session Class URL: http://vlsicad.ucsd.edu/courses/cse21-s14/ Notes 140608 Review Things to Know has
More informationComputer Sciences Department
Computer Sciences Department 1 Reference Book: INTRODUCTION TO THE THEORY OF COMPUTATION, SECOND EDITION, by: MICHAEL SIPSER Computer Sciences Department 3 ADVANCED TOPICS IN C O M P U T A B I L I T Y
More informationQuantum Computing. Joachim Stolze and Dieter Suter. A Short Course from Theory to Experiment. WILEY-VCH Verlag GmbH & Co. KGaA
Joachim Stolze and Dieter Suter Quantum Computing A Short Course from Theory to Experiment Second, Updated and Enlarged Edition WILEY- VCH WILEY-VCH Verlag GmbH & Co. KGaA Preface XIII 1 Introduction and
More informationSpace and Nondeterminism
CS 221 Computational Complexity, Lecture 5 Feb 6, 2018 Space and Nondeterminism Instructor: Madhu Sudan 1 Scribe: Yong Wook Kwon Topic Overview Today we ll talk about space and non-determinism. For some
More informationP, NP, NP-Complete. Ruth Anderson
P, NP, NP-Complete Ruth Anderson A Few Problems: Euler Circuits Hamiltonian Circuits Intractability: P and NP NP-Complete What now? Today s Agenda 2 Try it! Which of these can you draw (trace all edges)
More informationCSC 5170: Theory of Computational Complexity Lecture 5 The Chinese University of Hong Kong 8 February 2010
CSC 5170: Theory of Computational Complexity Lecture 5 The Chinese University of Hong Kong 8 February 2010 So far our notion of realistic computation has been completely deterministic: The Turing Machine
More informationA Short History of Computational Complexity
A Short History of Computational Complexity Lance Fortnow, Steve Homer Georgia Kaouri NTUAthens Overview 1936: Turing machine early 60 s: birth of computational complexity early 70 s: NP-completeness,
More informationAcknowledgments 2. Part 0: Overview 17
Contents Acknowledgments 2 Preface for instructors 11 Which theory course are we talking about?.... 12 The features that might make this book appealing. 13 What s in and what s out............... 14 Possible
More informationGreat Theoretical Ideas in Computer Science
15-251 Great Theoretical Ideas in Computer Science Lecture 28: A Computational Lens on Proofs December 6th, 2016 Evolution of proof First there was GORM GORM = Good Old Regular Mathematics Pythagoras s
More information-bit integers are all in ThC. Th The following problems are complete for PSPACE NPSPACE ATIME QSAT, GEOGRAPHY, SUCCINCT REACH.
CMPSCI 601: Recall From Last Time Lecture 26 Theorem: All CFL s are in sac. Facts: ITADD, MULT, ITMULT and DIVISION on -bit integers are all in ThC. Th The following problems are complete for PSPACE NPSPACE
More informationLecture 12: Interactive Proofs
princeton university cos 522: computational complexity Lecture 12: Interactive Proofs Lecturer: Sanjeev Arora Scribe:Carl Kingsford Recall the certificate definition of NP. We can think of this characterization
More informationIntroduction to Computational Complexity
Introduction to Computational Complexity Jiyou Li lijiyou@sjtu.edu.cn Department of Mathematics, Shanghai Jiao Tong University Sep. 24th, 2013 Computation is everywhere Mathematics: addition; multiplication;
More informationIntroduction to Quantum Computing
Introduction to Quantum Computing Part II Emma Strubell http://cs.umaine.edu/~ema/quantum_tutorial.pdf April 13, 2011 Overview Outline Grover s Algorithm Quantum search A worked example Simon s algorithm
More informationHarvard CS 121 and CSCI E-121 Lecture 22: The P vs. NP Question and NP-completeness
Harvard CS 121 and CSCI E-121 Lecture 22: The P vs. NP Question and NP-completeness Harry Lewis November 19, 2013 Reading: Sipser 7.4, 7.5. For culture : Computers and Intractability: A Guide to the Theory
More informationECS122A Handout on NP-Completeness March 12, 2018
ECS122A Handout on NP-Completeness March 12, 2018 Contents: I. Introduction II. P and NP III. NP-complete IV. How to prove a problem is NP-complete V. How to solve a NP-complete problem: approximate algorithms
More informationComputational Complexity: A Modern Approach. Draft of a book: Dated January 2007 Comments welcome!
i Computational Complexity: A Modern Approach Draft of a book: Dated January 2007 Comments welcome! Sanjeev Arora and Boaz Barak Princeton University complexitybook@gmail.com Not to be reproduced or distributed
More information6-1 Computational Complexity
6-1 Computational Complexity 6. Computational Complexity Computational models Turing Machines Time complexity Non-determinism, witnesses, and short proofs. Complexity classes: P, NP, conp Polynomial-time
More informationCHEMICAL GRAPH THEORY
CHEMICAL GRAPH THEORY SECOND EDITION Nenad Trinajstic, Ph.D. Professor of Chemistry The Rugjer Boskovic Institute Zagreb The Republic of Croatia CRC Press Boca Raton Ann Arbor London Tokyo TABLE OF CONTENTS
More informationLecture 22: Counting
CS 710: Complexity Theory 4/8/2010 Lecture 22: Counting Instructor: Dieter van Melkebeek Scribe: Phil Rydzewski & Chi Man Liu Last time we introduced extractors and discussed two methods to construct them.
More informationThe McEliece Cryptosystem Resists Quantum Fourier Sampling Attack
The McEliece Cryptosystem Resists Quantum Fourier Sampling Attack Cristopher Moore University of New Mexico and the Santa Fe Institute Joint work with Hang Dinh, University of Connecticut / Indiana, South
More informationIntroduction to Complexity Theory
Introduction to Complexity Theory Read K & S Chapter 6. Most computational problems you will face your life are solvable (decidable). We have yet to address whether a problem is easy or hard. Complexity
More informationNotes on Complexity Theory Last updated: November, Lecture 10
Notes on Complexity Theory Last updated: November, 2015 Lecture 10 Notes by Jonathan Katz, lightly edited by Dov Gordon. 1 Randomized Time Complexity 1.1 How Large is BPP? We know that P ZPP = RP corp
More informationCS278: Computational Complexity Spring Luca Trevisan
CS278: Computational Complexity Spring 2001 Luca Trevisan These are scribed notes from a graduate course on Computational Complexity offered at the University of California at Berkeley in the Spring of
More informationA difficult problem. ! Given: A set of N cities and $M for gas. Problem: Does a traveling salesperson have enough $ for gas to visit all the cities?
Intractability A difficult problem Traveling salesperson problem (TSP) Given: A set of N cities and $M for gas. Problem: Does a traveling salesperson have enough $ for gas to visit all the cities? An algorithm
More informationIntractability. A difficult problem. Exponential Growth. A Reasonable Question about Algorithms !!!!!!!!!! Traveling salesperson problem (TSP)
A difficult problem Intractability A Reasonable Question about Algorithms Q. Which algorithms are useful in practice? A. [von Neumann 1953, Gödel 1956, Cobham 1964, Edmonds 1965, Rabin 1966] Model of computation
More informationFrom Adversaries to Algorithms. Troy Lee Rutgers University
From Adversaries to Algorithms Troy Lee Rutgers University Quantum Query Complexity As in classical complexity, it is difficult to show lower bounds on the time or number of gates required to solve a problem
More informationPseudo-Deterministic Proofs
Pseudo-Deterministic Proofs Shafi Goldwasser 1, Ofer Grossman 2, and Dhiraj Holden 3 1 MIT, Cambridge MA, USA shafi@theory.csail.mit.edu 2 MIT, Cambridge MA, USA ofer.grossman@gmail.com 3 MIT, Cambridge
More informationContents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2
Contents Preface for the Instructor xi Preface for the Student xv Acknowledgments xvii 1 Vector Spaces 1 1.A R n and C n 2 Complex Numbers 2 Lists 5 F n 6 Digression on Fields 10 Exercises 1.A 11 1.B Definition
More informationLimits to Approximability: When Algorithms Won't Help You. Note: Contents of today s lecture won t be on the exam
Limits to Approximability: When Algorithms Won't Help You Note: Contents of today s lecture won t be on the exam Outline Limits to Approximability: basic results Detour: Provers, verifiers, and NP Graph
More informationData Structures and Algorithms
Data Structures and Algorithms Session 21. April 13, 2009 Instructor: Bert Huang http://www.cs.columbia.edu/~bert/courses/3137 Announcements Homework 5 due next Monday I m out of town Wed to Sun for conference
More informationPhysics and phase transitions in parallel computational complexity
Physics and phase transitions in parallel computational complexity Jon Machta University of Massachusetts Amherst and Santa e Institute Physics of Algorithms August 31, 2009 Collaborators Ray Greenlaw,
More informationQuantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 11: From random walk to quantum walk
Quantum algorithms (CO 781, Winter 2008) Prof. Andrew Childs, University of Waterloo LECTURE 11: From random walk to quantum walk We now turn to a second major topic in quantum algorithms, the concept
More informationComputational Complexity
Computational Complexity Problems, instances and algorithms Running time vs. computational complexity General description of the theory of NP-completeness Problem samples 1 Computational Complexity What
More informationChapter 1. Introduction
Chapter 1 Introduction Symbolical artificial intelligence is a field of computer science that is highly related to quantum computation. At first glance, this statement appears to be a contradiction. However,
More information8. INTRACTABILITY I. Lecture slides by Kevin Wayne Copyright 2005 Pearson-Addison Wesley. Last updated on 2/6/18 2:16 AM
8. INTRACTABILITY I poly-time reductions packing and covering problems constraint satisfaction problems sequencing problems partitioning problems graph coloring numerical problems Lecture slides by Kevin
More informationLecture Notes 17. Randomness: The verifier can toss coins and is allowed to err with some (small) probability if it is unlucky in its coin tosses.
CS 221: Computational Complexity Prof. Salil Vadhan Lecture Notes 17 March 31, 2010 Scribe: Jonathan Ullman 1 Interactive Proofs ecall the definition of NP: L NP there exists a polynomial-time V and polynomial
More informationThe Future. Currently state of the art chips have gates of length 35 nanometers.
Quantum Computing Moore s Law The Future Currently state of the art chips have gates of length 35 nanometers. The Future Currently state of the art chips have gates of length 35 nanometers. When gate lengths
More informationThe power and weakness of randomness (when you are short on time) Avi Wigderson Institute for Advanced Study
The power and weakness of randomness (when you are short on time) Avi Wigderson Institute for Advanced Study Plan of the talk Computational complexity -- efficient algorithms, hard and easy problems, P
More informationStrong ETH Breaks With Merlin and Arthur. Or: Short Non-Interactive Proofs of Batch Evaluation
Strong ETH Breaks With Merlin and Arthur Or: Short Non-Interactive Proofs of Batch Evaluation Ryan Williams Stanford Two Stories Story #1: The ircuit and the Adversarial loud. Given: a 1,, a K F n Want:
More informationHandbook of Logic and Proof Techniques for Computer Science
Steven G. Krantz Handbook of Logic and Proof Techniques for Computer Science With 16 Figures BIRKHAUSER SPRINGER BOSTON * NEW YORK Preface xvii 1 Notation and First-Order Logic 1 1.1 The Use of Connectives
More informationVector fields and phase flows in the plane. Geometric and algebraic properties of linear systems. Existence, uniqueness, and continuity
Math Courses Approved for MSME (2015/16) Mth 511 - Introduction to Real Analysis I (3) elements of functional analysis. This is the first course in a sequence of three: Mth 511, Mth 512 and Mth 513 which
More informationComplexity Theory. Jörg Kreiker. Summer term Chair for Theoretical Computer Science Prof. Esparza TU München
Complexity Theory Jörg Kreiker Chair for Theoretical Computer Science Prof. Esparza TU München Summer term 2010 Lecture 4 NP-completeness Recap: relations between classes EXP PSPACE = NPSPACE conp NP conp
More informationComplexity Theory. Jörg Kreiker. Summer term Chair for Theoretical Computer Science Prof. Esparza TU München
Complexity Theory Jörg Kreiker Chair for Theoretical Computer Science Prof. Esparza TU München Summer term 2010 2 Lecture 15 Public Coins and Graph (Non)Isomorphism 3 Intro Goal and Plan Goal understand
More informationCS 241 Analysis of Algorithms
CS 241 Analysis of Algorithms Professor Eric Aaron Lecture T Th 9:00am Lecture Meeting Location: OLB 205 Business Grading updates: HW5 back today HW7 due Dec. 10 Reading: Ch. 22.1-22.3, Ch. 25.1-2, Ch.
More informationInteractive Proofs. Merlin-Arthur games (MA) [Babai] Decision problem: D;
Interactive Proofs n x: read-only input finite σ: random bits control Π: Proof work tape Merlin-Arthur games (MA) [Babai] Decision problem: D; input string: x Merlin Prover chooses the polynomial-length
More informationLimitations of Algorithm Power
Limitations of Algorithm Power Objectives We now move into the third and final major theme for this course. 1. Tools for analyzing algorithms. 2. Design strategies for designing algorithms. 3. Identifying
More informationCOSE215: Theory of Computation. Lecture 20 P, NP, and NP-Complete Problems
COSE215: Theory of Computation Lecture 20 P, NP, and NP-Complete Problems Hakjoo Oh 2018 Spring Hakjoo Oh COSE215 2018 Spring, Lecture 20 June 6, 2018 1 / 14 Contents 1 P and N P Polynomial-time reductions
More informationAlgorithms: COMP3121/3821/9101/9801
NEW SOUTH WALES Algorithms: COMP3121/3821/9101/9801 Aleks Ignjatović School of Computer Science and Engineering University of New South Wales LECTURE 9: INTRACTABILITY COMP3121/3821/9101/9801 1 / 29 Feasibility
More informationLecture 8: Complete Problems for Other Complexity Classes
IAS/PCMI Summer Session 2000 Clay Mathematics Undergraduate Program Basic Course on Computational Complexity Lecture 8: Complete Problems for Other Complexity Classes David Mix Barrington and Alexis Maciel
More informationQuantum Computation. Leonard J. Schulman. Caltech
Quantum Computation Leonard J. Schulman Caltech Quantum Computation: what it is, what it isn t. Quantum mechanical effects enable efficient solution of classically intractable problems To appreciate this,
More informationTractable & Intractable Problems
Tractable & Intractable Problems We will be looking at : What is a P and NP problem NP-Completeness The question of whether P=NP The Traveling Salesman problem again Programming and Data Structures 1 Polynomial
More informationQuantum Algorithms Lecture #2. Stephen Jordan
Quantum Algorithms Lecture #2 Stephen Jordan Last Time Defined quantum circuit model. Argued it captures all of quantum computation. Developed some building blocks: Gate universality Controlled-unitaries
More informationCS Lecture 29 P, NP, and NP-Completeness. k ) for all k. Fall The class P. The class NP
CS 301 - Lecture 29 P, NP, and NP-Completeness Fall 2008 Review Languages and Grammars Alphabets, strings, languages Regular Languages Deterministic Finite and Nondeterministic Automata Equivalence of
More informationThe Class NP. NP is the problems that can be solved in polynomial time by a nondeterministic machine.
The Class NP NP is the problems that can be solved in polynomial time by a nondeterministic machine. NP The time taken by nondeterministic TM is the length of the longest branch. The collection of all
More informationCOSE215: Theory of Computation. Lecture 21 P, NP, and NP-Complete Problems
COSE215: Theory of Computation Lecture 21 P, NP, and NP-Complete Problems Hakjoo Oh 2017 Spring Hakjoo Oh COSE215 2017 Spring, Lecture 21 June 11, 2017 1 / 11 Contents 1 The classes P and N P Reductions
More informationQuantum Computation, NP-Completeness and physical reality [1] [2] [3]
Quantum Computation, NP-Completeness and physical reality [1] [2] [3] Compiled by Saman Zarandioon samanz@rutgers.edu 1 Introduction The NP versus P question is one of the most fundamental questions in
More informationThird Year Computer Science and Engineering, 6 th Semester
Department of Computer Science and Engineering Third Year Computer Science and Engineering, 6 th Semester Subject Code & Name: THEORY OF COMPUTATION UNIT-I CHURCH-TURING THESIS 1. What is a Turing Machine?
More informationComplexity Classes IV
Complexity Classes IV NP Optimization Problems and Probabilistically Checkable Proofs Eric Rachlin 1 Decision vs. Optimization Most complexity classes are defined in terms of Yes/No questions. In the case
More informationNP-Complete Problems and Approximation Algorithms
NP-Complete Problems and Approximation Algorithms Efficiency of Algorithms Algorithms that have time efficiency of O(n k ), that is polynomial of the input size, are considered to be tractable or easy
More informationLecture 7: The Satisfiability Problem
Lecture 7: The Satisfiability Problem 1 Satisfiability 1.1 Classification of Formulas Remember the 2 classifications of problems we have discussed in the past: Satisfiable and Valid. The Classification
More informationMathematics, Proofs and Computation
Mathematics, Proofs and Computation Madhu Sudan Harvard December 16, 2016 TIMC: Math, Proofs, Computing 1 of 25 Logic, Mathematics, Proofs Reasoning: Start with body of knowledge. Add to body of knowledge
More informationBounded Arithmetic, Expanders, and Monotone Propositional Proofs
Bounded Arithmetic, Expanders, and Monotone Propositional Proofs joint work with Valentine Kabanets, Antonina Kolokolova & Michal Koucký Takeuti Symposium on Advances in Logic Kobe, Japan September 20,
More informationAdvanced topic: Space complexity
Advanced topic: Space complexity CSCI 3130 Formal Languages and Automata Theory Siu On CHAN Chinese University of Hong Kong Fall 2016 1/28 Review: time complexity We have looked at how long it takes to
More informationChapter 8. NP and Computational Intractability. CS 350 Winter 2018
Chapter 8 NP and Computational Intractability CS 350 Winter 2018 1 Algorithm Design Patterns and Anti-Patterns Algorithm design patterns. Greedy. Divide-and-conquer. Dynamic programming. Duality. Reductions.
More informationMind the gap Solving optimization problems with a quantum computer
Mind the gap Solving optimization problems with a quantum computer A.P. Young http://physics.ucsc.edu/~peter Work supported by Talk at Saarbrücken University, November 5, 2012 Collaborators: I. Hen, E.
More informationCOMPUTATIONAL COMPLEXITY THEORY
500 COMPUTATIONAL COMPLEXITY THEORY A. M. Turing, Collected Works: Mathematical Logic, R. O. Gandy and C. E. M. Yates (eds.), Amsterdam, The Nethrelands: Elsevier, Amsterdam, New York, Oxford, 2001. S.
More informationComputational Complexity CSCI-GA Subhash Khot Transcribed by Patrick Lin
Computational Complexity CSCI-GA 3350 Subhash Khot Transcribed by Patrick Lin Abstract. These notes are from a course in Computational Complexity, as offered in Spring 2014 at the Courant Institute of
More informationSiegel s theorem, edge coloring, and a holant dichotomy
Siegel s theorem, edge coloring, and a holant dichotomy Tyson Williams (University of Wisconsin-Madison) Joint with: Jin-Yi Cai and Heng Guo (University of Wisconsin-Madison) Appeared at FOCS 2014 1 /
More informationCOMP-330 Theory of Computation. Fall Prof. Claude Crépeau. Lecture 2 : Regular Expressions & DFAs
COMP-330 Theory of Computation Fall 2017 -- Prof. Claude Crépeau Lecture 2 : Regular Expressions & DFAs COMP 330 Fall 2017: Lectures Schedule 1-2. Introduction 1.5. Some basic mathematics 2-3. Deterministic
More informationA Working Knowledge of Computational Complexity for an Optimizer
A Working Knowledge of Computational Complexity for an Optimizer ORF 363/COS 323 Instructor: Amir Ali Ahmadi 1 Why computational complexity? What is computational complexity theory? It s a branch of mathematics
More informationLecture 24: How to become Famous with P and NP
Lecture 4: How to become Famous with P and NP Agenda for today s class: The complexity class P The complexity class NP NP-completeness The P =? NP problem Major extra-credit problem (due: whenever) Fun
More informationComplexity Theory. Jörg Kreiker. Summer term Chair for Theoretical Computer Science Prof. Esparza TU München
Complexity Theory Jörg Kreiker Chair for Theoretical Computer Science Prof. Esparza TU München Summer term 2010 Lecture 6 conp Agenda conp the importance of P vs. NP vs. conp neither in P nor NP-complete:
More informationLecture 5. 1 Review (Pairwise Independence and Derandomization)
6.842 Randomness and Computation September 20, 2017 Lecture 5 Lecturer: Ronitt Rubinfeld Scribe: Tom Kolokotrones 1 Review (Pairwise Independence and Derandomization) As we discussed last time, we can
More informationAn Introduction to Quantum Information and Applications
An Introduction to Quantum Information and Applications Iordanis Kerenidis CNRS LIAFA-Univ Paris-Diderot Quantum information and computation Quantum information and computation How is information encoded
More informationAnnouncements. Analysis of Algorithms
Announcements Analysis of Algorithms Piyush Kumar (Lecture 9: NP Completeness) Welcome to COP 4531 Based on Kevin Wayne s slides Programming Assignment due: April 25 th Submission: email your project.tar.gz
More informationComputational Complexity Theory
Computational Complexity Theory Marcus Hutter Canberra, ACT, 0200, Australia http://www.hutter1.net/ Assumed Background Preliminaries Turing Machine (TM) Deterministic Turing Machine (DTM) NonDeterministic
More informationQuantum Computing. Part I. Thorsten Altenkirch
Quantum Computing Part I Thorsten Altenkirch Is Computation universal? Alonzo Church - calculus Alan Turing Turing machines computable functions The Church-Turing thesis All computational formalisms define
More information