Efficient Algorithms for Euclidean Traveling Salesman Problem in Narrow Strips

Core Concepts

For planar point sets contained in narrow strips, the complexity of the Euclidean Traveling Salesman Problem depends on the strip width. Efficient fixed-parameter tractable algorithms are presented that exploit the geometric structure of the problem.

Abstract

The paper investigates the complexity of the Euclidean Traveling Salesman Problem (TSP) for planar point sets that are contained in narrow strips. The key findings are:

For points with distinct integer x-coordinates in a strip of width δ ≤ 2√2, a shortest bitonic tour is guaranteed to be a shortest tour overall. This bound is shown to be best possible.

For sparse point sets, where each 1 × δ rectangle inside the strip contains O(1) points, an efficient fixed-parameter tractable algorithm is presented. The algorithm has a running time of 2O(√δ)n + O(δ2n2) and generalizes to higher dimensions.

For random point sets drawn uniformly from the strip [0, n] × [0, δ], a similar algorithm has an expected running time of 2O(√δ)n, which is linear if δ = O(1).

The paper provides a detailed analysis of how the complexity of Euclidean TSP depends on the strip width δ, demonstrating that for "almost 1-dimensional" point sets, the problem becomes significantly easier to solve exactly compared to the general 2D case.

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Key Insights Distilled From

Euclidean TSP in Narrow Strips

by Henk... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2003.09948.pdf

Deeper Inquiries

How can the insights from this work on narrow strips be extended to other geometric restrictions on the input point set, such as bounded aspect ratio or bounded spread

The insights gained from studying Euclidean TSP in narrow strips can be extended to other geometric restrictions on the input point set. For example, when considering bounded aspect ratio, where the ratio of the longest to the shortest side of the bounding box of the point set is limited, similar techniques can be applied. By analyzing how the complexity of Euclidean TSP changes with the aspect ratio, one can derive bounds on the optimal tours and tonicity requirements. This can lead to the development of specialized algorithms that exploit the geometric properties of point sets with bounded aspect ratios to efficiently solve the TSP.

What are the implications of these results on the design of practical TSP solvers that can exploit the geometric structure of real-world instances

The results obtained from this research have significant implications for the design of practical TSP solvers that can leverage the geometric structure of real-world instances. By understanding how the complexity of Euclidean TSP varies with different geometric constraints, such as narrow strips or bounded aspect ratios, algorithm designers can tailor their approaches to exploit these properties. This can lead to the development of specialized algorithms that are optimized for specific geometric configurations, resulting in more efficient and effective TSP solvers for real-world applications. Additionally, the insights gained from this work can inform the development of heuristic approaches that take into account the geometric structure of the input point sets to quickly approximate optimal solutions.

Can the techniques developed in this paper be applied to other optimization problems on point sets in narrow regions, beyond the Euclidean TSP

The techniques developed in this paper for Euclidean TSP in narrow strips can be applied to other optimization problems on point sets in narrow regions beyond TSP. For instance, problems like the Traveling Salesman Path (TSP with a specified starting point but no requirement to return to it) or the Minimum Spanning Tree (MST) can benefit from similar analyses in narrow geometric regions. By adapting the concepts of bitonicity, tonicity, and optimal tour properties to these different optimization problems, researchers can develop specialized algorithms that exploit the geometric constraints to efficiently solve these problems. This extension of techniques to other optimization problems in narrow regions opens up new avenues for research in geometric optimization and algorithm design.