TL;DR
This paper generalises the existing PTAS for complete graphs from a fixed to an arbitrary number of intervals and disprove an existing conjecture, which states that every instance of BIA admits a solution satisfying at least three quarters of all edges.
In this paper we study the fractional vertex cover problem on trees in two related models: online and incremental. In the online model, the vertices of the tree are known a priori and the edges arrive one at a time. The goal is to maintain a fractional vertex cover of the tree, i.e., an assignment of fractional weights from [0,1] to the vertices such that the weights of endpoints of every edge sum up to at least one. After each edge arrival, we need to modify the fractional vertex cover to cover the new edge as well. However, we can only increase the values assigned to vertices. The problem was studied before (in the vertex arrival model) by Wang and Wong, who motivated it as a generalization of the ski-rental problem, but also (more importantly) by its close connection to the dual online matching problem. They presented a 1.901-competitive algorithm for general graphs in the vertex arrival model. We present an $\frac{11}{6} \approx 1.83$-competitive algorithm for trees in the more general edge arrival model. In addition, we study the fractional vertex cover problem in an incremental model, where we again seek a fractional vertex cover after every update, but all the updates to the tree are known to the algorithm a priori. In this model, we give a 1.5-competitive algorithm and provide a matching lower bound.
It is proved that Minimal-to-Maximal Conversion Search is in fact not output-polynomial and the lower bound construction motivates a more detailed analysis of how certain heuristic choices in the algorithm design affect the running time.
We study the shortest-path problem on graphs with positive real-valued edge weights. Given a source vertex $s$ and a target vertex $t$, the goal is to calculate the length of the shortest path from $s$ to $t$. We are particularly interested in instances that can be solved in sublinear time. Recently, Haeupler, Hlad\'ik, Rozho\v{n}, Tarjan, and T\v{e}tek proved that (a version of) the bidirectional Dijkstra's algorithm is instance-optimal on positively weighted multigraphs, both directed and undirected, considering the number of vertices and edges queried by the algorithm. However, multigraphs are not the canonical setting for the shortest-path problem. The problem is typically formulated on simple graphs without loops and parallel edges. They therefore left as an open problem whether bidirectional Dijkstra remains instance-optimal on simple weighted graphs. We answer this question, but for simple graphs, the answer is more complex, depending on the setting. We show that bidirectional Dijkstra is still instance-optimal on simple undirected weighted graphs under the order-oblivious model, where incident edges are given in a random order. In contrast, under the order-dependent model, where incident edges have a given order, we show that bidirectional Dijkstra is not instance-optimal. For simple directed weighted graphs, we show that bidirectional Dijkstra is not instance-optimal under either the order-oblivious or the order-dependent model, being off by a factor of $\Theta(m/n)$ in both cases. We further show that no algorithm can have instance-optimality ratio $o(m/n)$ under the order-dependent model, or under the order-oblivious model when $m=O(n\sqrt{n})$. On the positive side, the above results imply that bidirectional Dijkstra is instance-optimal up to logarithmic factors on all sparse directed and undirected graphs satisfying $m/n=\log^{O(1)} n$.
For graph instances both for the min-max and the min-disagreement objectives, this work proves approximation guarantees that are substantially better than the bounds achievable for general graphs.
This paper addresses the minimum linear arrangement problem, a classical graph layout problem in which the objective is to minimize the sum of absolute differences between the labels assigned to adjacent vertices in undirected graphs. The paper introduces small boundary (SB(k)), a family of linear-time greedy heuristics that guide vertex labeling through a prioritization scheme based on the structure of labeled and k levels of unlabeled vertex neighborhoods. An extensive experimental evaluation on 26 large-scale real-world graphs demonstrates that the proposed heuristic with k=2 consistently outperforms 19 established low-cost graph-theoretic reordering algorithms, including the leading constructive method used within a high-performing metaheuristic algorithm for the problem. Additionally, the SB(2) heuristic outperforms this high-performing metaheuristic algorithm while requiring up to 800 times less computational effort. The paper further shows that integrating SB(2) as the initialization procedure within this metaheuristic yields improved solution quality, highlighting its effectiveness as both a standalone heuristic and a high-quality initializer. Overall, the proposed approach provides a fast, scalable, and practical solution for tackling MinLA on large-scale graphs.