Search arXivSearch

arXiv · 2606.31907

Improved Algorithms for Bounded-Degree (Subset) Traveling Salesman Problems

Abstract

We present improved algorithms for several bounded-degree traveling salesman problems. In the bounded-degree traveling salesman path problem (BDTSPP), given a weighted graph G=(V,E), two endpoints s and t, and degree bounds b_v for all v, the goal is to find a minimum-cost subgraph of G that admits an Eulerian s-t path and in which each vertex v has degree at most b_v. Since deciding feasibility is already NP-hard for this problem, previous work gave a bicriteria approximation algorithm. However, that algorithm provides only a multiplicative guarantee on the degree violation, and it was left open whether additive violation is possible. We answer this open question affirmatively by giving a new bicriteria approximation algorithm with additive degree violation. The cost approximation ratio is improved as well, now matching that of Hoogeveen's analysis of the Christofides-Serdyukov algorithm. This improvement relies on a new lemma that enables the use of a bounded-degree minimum spanning tree, rather than a bounded-degree Steiner tree, as a starting point for the algorithm. The lemma compares the cost and degrees of the tree against those of an integral optimum for the bounded-degree TSP at hand, rather than those of a fractional optimum. Our lemma brings improvement to the circuit version (BDTSP) as well: we give a bicriteria algorithm that matches the previous cost approximation ratio while reducing the additive degree violation to +2, which is best possible. Subset TSP is a generalization of the standard "all-vertices" TSP, in which only a specified subset of vertices is required to be visited. We present improvements for both the circuit and the path versions. For the subset path problem (BDSTSPP), we present the first bicriteria approximation algorithm with additive degree violation; for the subset circuit problem (BDSTSP), we give an improved cost approximation ratio.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jongseo Lee, Jaehyeok Kwak, Hyung-Chan An. 2026-06-30. Improved Algorithms for Bounded-Degree (Subset) Traveling Salesman Problems. https://arxiv.org/abs/2606.31907

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Beyond Kruskal: Polynomial-Time Tensor Decomposition under the Lovitz-Petrov Condition

Identifiability criteria certify that a given tensor decomposition is a unique rank decomposition. Kruskal's classical condition is one of the best-known deterministic criteria for identifiability. However, no polynomial-time decomposition algorithm is known under the Kruskal condition, and verifying the condition itself is NP-hard. Lovitz and Petrov introduced a strictly more general identifiability condition which, in contrast, is polynomial-time verifiable, but no polynomial-time decomposition algorithm was previously known under this condition. We give a polynomial-time algorithm for tensor decomposition under the Lovitz--Petrov condition. Moreover, combining our algorithm with polynomial-time verification of the Lovitz--Petrov condition yields an efficient end-to-end certification procedure: after computing a decomposition, one can deterministically certify in polynomial time that it is unique and therefore of minimum rank. This contrasts with an arbitrary tensor decomposition, which certifies only an upper bound on the tensor rank, while determining tensor rank is NP-hard in general.

cs.DS

Poisson Exchange Beyond Submodularity: Effective Approximation Algorithms for Offline and Online Subset Selection over Matroids

Over the past decade, a growing body of research has shown that $γ$-weak submodularity broadly arises in numerous subset selection tasks, including feature selection, neural network pruning, and video summarization. Despite its prevalence, maximizing a $γ$-weakly submodular function subject to a general matroid constraint remains challenging. To date, the only known approximation guarantee is the conservative $(1+1/γ)^{-2}$ factor established by \citet{chen2018weakly}. To improve upon this result, this paper proposes a novel algorithm called \MGPE, which repeatedly performs maximum-gain local exchanges through careful control of a non-homogeneous Poisson clock, and proves that this \MGPE\ can attain an approximation ratio arbitrarily close to $ρ_γ=1-\left(γ/(2-γ)\right)^{ \frac{γ^2}{2(1-γ)} }$. In sharp contrast to the previous guarantee, our obtained factor $ρ_γ$ not only strictly improves upon $(1+1/γ)^{-2}$ for every $γ\in(0,1]$, but also can asymptotically approach the optimal $(1-1/e)$-approximation for submodular maximization as $γ\to1$. Furthermore, we surprisingly find that when the matroid constraint reduces to a cardinality or the objective satisfies the stronger notion of $α$-weak DR-submodularity, \MGPE\ can automatically recover the tight approximation ratios of $1-e^{-γ}$ and $1-e^{-α}$, respectively. Here, $α\in(0,1]$ denotes the DR ratio.

cs.DS

Approximating Prize-Collecting TSP below 1.556

The prize-collecting traveling salesperson problem is a variant of the metric traveling salesperson problem in which vertices may be left unvisited by paying their associated penalties. The objective is to minimize the length of the tour plus the total penalty of the unvisited vertices. Blauth, Klein, and Nägele gave the previously best-known LP-relative $1.599$-approximation. We show that a simpler version of their algorithm, obtained by omitting the splitting-off preprocessing before the tree decomposition, has an LP-relative approximation ratio of $1.555761$. The improvement comes entirely from a new analysis of the parity-correction step: a simple analysis already gives $1.56$, and the stated factor follows from a numerical parameter search with exact verification.

cs.DS