Resource Constrained Pathfinding with A* and Negative Weights
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Abstract (arXiv)
Constrained pathfinding is a well-studied, yet challenging network optimisation problem that can be seen in a broad range of real-world applications. Pathfinding with multiple resource limits, which is known as the Resource Constrained Shortest Path Problem (RCSP), aims to plan a cost-optimum path subject to limited usage of resources. Given the recent advances in constrained and multi-criteria search with A*, this paper introduces a new resource constrained search framework on the basis of A* to tackle RCSP in large networks, even in the presence of negative cost and negative resources. We empirically evaluate our new algorithm on a set of large instances and show up to two orders of magnitude faster performance compared to state-of-the-art RCSP algorithms in the literature.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 83 references that are in the map. First posted on arXiv 14 Mar 2025.
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References · 14
- 2023New Bounds for Matrix Multiplication: from Alpha to Omegacited by 34
- 2024More Asymmetry Yields Faster Matrix Multiplicationcited by 27
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- 2018An Equivalence Class for Orthogonal Vectorscited by 5
- 2022Enhanced Multi-Objective A* Using Balanced Binary Search Treescited by 5
- 2023Dynamic Dynamic Time Warpingcited by 5
- 2015If the Current Clique Algorithms are Optimal, so is Valiant's Parsercited by 4
- 2018More Consequences of Falsifying SETH and the Orthogonal Vectors Conjecturecited by 3
- 2018SETH Says: Weak Fr'echet Distance is Faster, but only if it is Continuous and in One Dimensioncited by 2
- 2019Graph pattern detection: Hardness for all induced patterns and faster non-induced cyclescited by 1
- 2022Induced Cycles and Paths Are Harder Than You Thinkcited by 1
- 2022Distributed Maximal Matching and Maximal Independent Set on Hypergraphscited by 1
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