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Functional Correctness of C Implementations of Dijkstra’s, Kruskal’s, and Prim’s Algorithms

Authors: Anshuman Mohan; Wei Xiang Leow; Aquinas Hobor;

Functional Correctness of C Implementations of Dijkstra’s, Kruskal’s, and Prim’s Algorithms

Abstract

AbstractWe develop machine-checked verifications of the full functional correctness of C implementations of the eponymous graph algorithms of Dijkstra, Kruskal, and Prim. We extend Wang et al.’s CertiGraph platform to reason about labels on edges, undirected graphs, and common spatial representations of edge-labeled graphs such as adjacency matrices and edge lists. We certify binary heaps, including Floyd’s bottom-up heap construction, heapsort, and increase/decrease priority.Our verifications uncover subtle overflows implicit in standard textbook code, including a nontrivial bound on edge weights necessary to execute Dijkstra’s algorithm; we show that the intuitive guess fails and provide a workable refinement. We observe that the common notion that Prim’s algorithm requires a connected graph is wrong: we verify that a standard textbook implementation of Prim’s algorithm can compute minimum spanning forests without finding components first. Our verification of Kruskal’s algorithm reasons about two graphs simultaneously: the undirected graph undergoing MSF construction, and the directed graph representing the forest inside union-find. Our binary heap verification exposes precise bounds for the heap to operate correctly, avoids a subtle overflow error, and shows how to recycle keys to avoid overflow.

Keywords

Dijkstra's algorithm, separation logic, formal proof, Prim's algorithm, Kruskal's algorithm, Coq, graph-manipulating programs, formal verification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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