'Optimal tour' presentation slideshows

Case Studies: Bin Packing & The Traveling Salesman Problem

1.48k views • 132 slides

Case Studies: Bin Packing & The Traveling Salesman Problem

903 views • 89 slides

Approximation algorithms for TSP with neighborhoods in the plane

Approximation algorithms for TSP with neighborhoods in the plane. R90922026 ??? R90922038 ???. The Problem. TSPN: Euclidean TSP with neighborhoods in the plane, i.e., dimention = 2

834 views • 67 slides

15-251

15-251. Great Theoretical Ideas in Computer Science. Victor Adamchik Danny Sleator. Graphs II. Lecture 19, March 25, 2010. Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic).

579 views • 45 slides

15-251

572 views • 41 slides

Chapter 9

Chapter 9. Approximation Algorithms. NP-Complete Problem. Enumeration Branch an Bound Greedy Approximation PTAS K-Approximation No Approximation. Approximation algorithm.

758 views • 39 slides

CS 2800 Discrete Structures

635 views • 38 slides

15-251

15-251. Great Theoretical Ideas in Computer Science. Upcoming Events. Quiz this Thursday. Test in 2 weeks. Graphs II. Lecture 21 (November 6, 2007). Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic).

643 views • 35 slides

COMPSCI 102

COMPSCI 102. Introduction to Discrete Mathematics. Graphs II. Lecture 20 (November 7, 2007). Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic). 2. Every two nodes of G are joined by a unique path.

592 views • 34 slides

COMPSCI 102

COMPSCI 102. Introduction to Discrete Mathematics. Graphs II. Lecture 20 (November 7, 2007). Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic). 2. Every two nodes of G are joined by a unique path.

342 views • 34 slides

COMPSCI 102

COMPSCI 102. Introduction to Discrete Mathematics. Graphs II. Lecture 18 (November 8, 2010). Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic). 2. Every two nodes of G are joined by a unique path.

342 views • 34 slides

15-251

15-251. Some. Great Theoretical Ideas in Computer Science. for. Graphs II. Lecture 18 (March 19, 2009). Recap. Theorem: Let G be a graph with n nodes and e edges. The following are equivalent:. 1. G is a tree (connected, acyclic).

311 views • 31 slides

Polynomial Time Approximation Schemes for Euclidean TSP

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Discrete Structures for Computer Science

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Approximate Algorithms & Repetitive Nearest Neighbor

Approximate Algorithms & Repetitive Nearest Neighbor. Notes 28 - Sections 6.6 & 6.7. Essential Learnings. Students will understand and be able to use approximate algorithms and the repetitive nearest neighbor algorithm to solve TSP problems. Approximate Algorithms.

474 views • 19 slides

Srikanth Vadada Kishan Kumar B P Fall 2010 - CSE 5311

Solving Travelling Salesman Problem for Metric Graphs using MST Heuristic. Srikanth Vadada Kishan Kumar B P Fall 2010 - CSE 5311. Presentation Outline. TSP Problem Overview TSP Approximation Algorithms Project Modules Project Demo. TSP Problem Overview. TSP is a Graph Problem

278 views • 14 slides

Approximation Algorithms

Approximation Algorithms. Ola Svensson. Course Information . Goal: Learn the techniques used by studying famous applications Graduate Course FDD3390 6 Points One Lecture a Week Examination Topics. Teacher Information. Ola Svensson Email: osven@kth.se Room: 1444

228 views • 11 slides