Book contents
- Frontmatter
- Preface
- Contents
- 1 Introduction
- 2 Linear Programming Relaxations of the Symmetric TSP
- 3 Linear Programming Relaxations of the Asymmetric TSP
- 4 Duality, Cuts, and Uncrossing
- 5 Thin Trees and Random Trees
- 6 Asymmetric Graph TSP
- 7 Constant-Factor Approximation for the Asymmetric TSP
- 8 Algorithms for Subtour Cover
- 9 Asymmetric Path TSP
- 10 Parity Correction of Random Trees
- 11 Proving the Main Payment Theorem for Hierarchies
- 12 Removable Pairings
- 13 Ear-Decompositions, Matchings, and Matroids
- 14 Symmetric Path TSP and T-Tours
- 15 Best-of-Many Christofides and Variants
- 16 Path TSP by Dynamic Programming
- 17 Further Results, Related Problems
- 18 State of the Art, Open Problems
- Bibliography
- Index
16 - Path TSP by Dynamic Programming
Published online by Cambridge University Press: 14 November 2024
- Frontmatter
- Preface
- Contents
- 1 Introduction
- 2 Linear Programming Relaxations of the Symmetric TSP
- 3 Linear Programming Relaxations of the Asymmetric TSP
- 4 Duality, Cuts, and Uncrossing
- 5 Thin Trees and Random Trees
- 6 Asymmetric Graph TSP
- 7 Constant-Factor Approximation for the Asymmetric TSP
- 8 Algorithms for Subtour Cover
- 9 Asymmetric Path TSP
- 10 Parity Correction of Random Trees
- 11 Proving the Main Payment Theorem for Hierarchies
- 12 Removable Pairings
- 13 Ear-Decompositions, Matchings, and Matroids
- 14 Symmetric Path TSP and T-Tours
- 15 Best-of-Many Christofides and Variants
- 16 Path TSP by Dynamic Programming
- 17 Further Results, Related Problems
- 18 State of the Art, Open Problems
- Bibliography
- Index
Summary
Traub and Vygen used recursive dynamic programming to obtain a (3/2+ε)-approximation algorithm for Path TSP for any ε>0. This approach was then improved and simplified by Zenklusen, who obtained a 3/2-approximation for Path TSP. After discussing the dynamic programming approach in a simple context, we present Zenklusen’s algorithm.
Then we present a black-box reduction from Path TSP to Symmetric TSP, similar to the one proposed by Traub, Vygen, and Zenklusen. This shows that the former is not much harder to approximate than the latter. This implies the currently best-known approximation guarantees for Path TSP and the special case Graph Path TSP. Our new proof, again based on dynamic programming, actually yields the same result even for a more general problem, which we call Multi-Path TSP.
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- Chapter
- Information
- Approximation Algorithms for Traveling Salesman Problems , pp. 351 - 380Publisher: Cambridge University PressPrint publication year: 2024