1 The Euclidean Travelling Salesman Problem Peter Eades Professor of Software Technology University of Sydney.

Slides:



Advertisements
Similar presentations
Approximating Graphic TSP by Matchings Tobias Mömke and Ola Svensson KTH Royal Institute of Technology Sweden.
Advertisements

Shortest Paths Text Discrete Mathematics and Its Applications (5 th Edition) Kenneth H. Rosen Chapter 9.6 Based on slides from Chuck Allison, Michael T.
Traveling Salesman Problem By Susan Ott for 252. Overview of Presentation Brief review of TSP Examples of simple Heuristics Better than Brute Force Algorithm.
CPE702 Complexity Classes Pruet Boonma Department of Computer Engineering Chiang Mai University Based on Material by Jenny Walter.
What is the computational cost of automating brilliance or serendipity? (Computational complexity and P vs NP question) COS 116: 4/12/11 Sanjeev Arora.
PCPs and Inapproximability Introduction. My T. Thai 2 Why Approximation Algorithms  Problems that we cannot find an optimal solution.
Rohit Ray ESE 251. The goal of the Traveling Salesman Problem (TSP) is to find the most economical way to tour of a select number of “cities” with the.
Introduction to Approximation Algorithms Lecture 12: Mar 1.
Math443/543 Mathematical Modeling and Optimization
1 NP-Completeness Objectives: At the end of the lesson, students should be able to: 1. Differentiate between class P, NP, and NPC 2. Reduce a known NPC.
The Theory of NP-Completeness
CS3381 Des & Anal of Alg ( SemA) City Univ of HK / Dept of CS / Helena Wong 8. Approximation Alg Approximation.
Minimum Back-Walk-Free Latency Problem Yaw-Ling Lin Dept Computer Sci. & Info. Management, Providence University, Taichung, Taiwan.
1 CSE 417: Algorithms and Computational Complexity Winter 2001 Lecture 22 Instructor: Paul Beame.
Network Optimization Problems: Models and Algorithms
Solution methods for Discrete Optimization Problems.
1 Integrality constraints Integrality constraints are often crucial when modeling optimizayion problems as linear programs. We have seen that if our linear.
1 Trends in Mathematics: How could they Change Education? László Lovász Eötvös Loránd University Budapest.
The Travelling Salesman Problem (TSP)
Programming & Data Structures
Hon Wai Leong, NUS (CS6234, Spring 2009) Page 1 Copyright © 2009 by Leong Hon Wai CS6234 Lecture 1 -- (14-Jan-09) “Introduction”  Combinatorial Optimization.
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
Scott Perryman Jordan Williams.  NP-completeness is a class of unsolved decision problems in Computer Science.  A decision problem is a YES or NO answer.
Applications of discrete mathematics: Formal Languages (computer languages) Compiler Design Data Structures Computability Automata Theory Algorithm Design.
Complexity Classes (Ch. 34) The class P: class of problems that can be solved in time that is polynomial in the size of the input, n. if input size is.
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
FACULTY OF ENGINEERING & INFORMATION TECHNOLOGIES P, NP, and Complexity Six fundamental facts One rule of thumb Three fundamental notions One fundamental.
Tonga Institute of Higher Education Design and Analysis of Algorithms IT 254 Lecture 8: Complexity Theory.
RESOURCES, TRADE-OFFS, AND LIMITATIONS Group 5 8/27/2014.
Finite Mathematics, Feodor F. Dragan, Kent State University 1.
A few m3 tips. Speed Tuning 1.Algorithm 2.Data structures 3.Low level code string streetName1, streetName2; if (streetName1 != streetName2) {... int streetId1,
Instructor: Shengyu Zhang 1. Tractable While we have introduced many problems with polynomial-time algorithms… …not all problems enjoy fast computation.
CSCI 3160 Design and Analysis of Algorithms Chengyu Lin.
Cliff Shaffer Computer Science Computational Complexity.
CSE 589 Part VI. Reading Skiena, Sections 5.5 and 6.8 CLR, chapter 37.
Hard problems in computer science Prof. Noah Snavely CS1114
Lecture 6 NP Class. P = ? NP = ? PSPACE They are central problems in computational complexity.
Beauty and Joy of Computing Limits of Computing Ivona Bezáková CS10: UC Berkeley, April 14, 2014 (Slides inspired by Dan Garcia’s slides.)
Limits to Computation How do you analyze a new algorithm? –Put it in the form of existing algorithms that you know the analysis. –For example, given 2.
The Maximum Traveling Salesman Problem under Polyhedral Norms Alexander Barvinok, David S. Johnson, Gerhard J. Woeginger and Russell Woodroffe Integer.
CPS Computational problems, algorithms, runtime, hardness (a ridiculously brief introduction to theoretical computer science) Vincent Conitzer.
Nirmalya Roy School of Electrical Engineering and Computer Science Washington State University Cpt S 223 – Advanced Data Structures Math Review 1.
Lecture 25 NP Class. P = ? NP = ? PSPACE They are central problems in computational complexity.
Algorithms for hard problems Introduction Juris Viksna, 2015.
David Luebke 1 2/18/2016 CS 332: Algorithms NP Completeness Continued: Reductions.
OR Integer Programming ( 정수계획법 ). OR
Efficient Point Coverage in Wireless Sensor Networks Jie Wang and Ning Zhong Department of Computer Science University of Massachusetts Journal of Combinatorial.
Part II General Integer Programming II.1 The Theory of Valid Inequalities 1.
Approximation Algorithms for the Traveling Salesman Problem Shayan Oveis Gharan.
Copyright © 2008 Pearson Education, Inc. Slide 13-1 Unit 13B The Traveling Salesman Problem.
Traveling Salesman Problem DongChul Kim HwangRyol Ryu.
Exhaustive search Exhaustive search is simply a brute- force approach to combinatorial problems. It suggests generating each and every element of the problem.
S5.40. Module Structure 30% practical tests / 70% written exam 3h lectures / week (except reading week) 3 x 2h of computer labs (solving problems practicing.
Brute Force A straightforward approach, usually based directly on the problem’s statement and definitions of the concepts involved Examples: Computing.
Optimization problems such as
GCD and Optimization Problem
1 The Travelling Salesman Problem (TSP) H.P. Williams Professor of Operational Research London School of Economics.
Great Ideas in Computing Complexity Theory
Shortest Paths Discrete Mathematics and Its Applications (7th Edition)
Discrete Mathematics and Its Applications (5th Edition)
… The traveling salesman problem on the WWW 49 cities Dantzig, Fulkerson, and Johnson (1954) 120 cities Groetschel (1977). Padberg and Rinaldi.
Approximation Algorithms
3. Brute Force Selection sort Brute-Force string matching
Discrete Mathematics and Its Applications (5th Edition)
Milestone 4: Courier Company
3. Brute Force Selection sort Brute-Force string matching
Approximation Algorithms
Shortest Paths Discrete Mathematics and Its Applications (7th Edition)
3. Brute Force Selection sort Brute-Force string matching
Presentation transcript:

1 The Euclidean Travelling Salesman Problem Peter Eades Professor of Software Technology University of Sydney

2 The problem A salesman’s territory consists of n cities. He must tour all the cities, and minimise travel time. Melbourne Sydney Brisbane Adelaide Byron Bay Melbourne Sydney Brisbane Adelaide Byron Bay 4571km 4730km We want an algorithm that gives a minimum tour.

3 Significant papers  This means that it is unlikely that we there is an efficient algorithm that returns an optimal result. Rough idea: If we could solve the Euclidean Travelling Salesman problem, then we could solve a lot of other problems. But these other problems are known to have defeated many top scientists. Therefore the Euclidean travelling salesman problem is hard. 1.C. Papadimitriou, The Euclidean travelling salesman problem in general is NP-complete, Math. Programming 14, , Three significant papers:

4 Significant papers Two significant papers:  In theory, there is an efficient algorithm that returns a result that is very close to optimal.  Given ε>0, there is an algorithm that runs in time O(n 1/ε ) and returns a travelling salesman tour that has distance at most (1+ε) times the minimum possible distance.  There is an almost polynomial-time algorithm that gets an almost-optimal solution. 2.S. Arora, "Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems", JACM 45, 753–782, 1998.

5 ε Closeness to optimal solution Runtime Arora’s paper

6 Significant papers A significant book:  This book summarises the practical breakthroughs, mainly from papers written in the 1990s. In practice, optimal solutions for problems with about cities (eg, Sweden) can be found. The methods used are basically variations of Integer Linear Programming. 3.D. Applegate, R. Bixby, V. Chvátal & W. Cook, The Traveling Salesman Problem: A Computational Study, Princeton University Press 2006.

7 Where to publish? The classical venues for algorithmics are good for papers about the Euclidean Travelling Salesman Problem: Journals (ERA rank A*)  Journal of the ACM  Mathematics of Operations Research Conferences (ERA rank A)  SODA (Symposium on Discrete Algorithms)  IPCO (Integer Programming and Combinatorial Optimisation)

8 Significant groups A significant research group: Located near Rutger’s University in New Jersey. Mostly a “Virtual Organisation”. Partners are: Rutgers University, Princeton University, AT&T Labs - Research, Bell Labs, Telcordia Technologies and NEC Laboratories America. Long history of contributions to Euclidean Travelling Salesman problem, including the “Grand Challenge”. Most famous researchers in this area are members.