Incremental Shortest Paths in Almost Linear Time via a Modified Interior Point Method

We give an algorithm that takes a directed graph $G$ undergoing $m$ edge insertions with lengths in $[1, W]$, and maintains $(1+ε)$-approximate shortest path distances from a fixed source $s$ to all other vertices. The algorithm is deterministic and runs in total time $m^{1+o(1)}\log W$, for any $ε> \exp(-(\log m)^{0.99})$. This is achieved by designing a nonstandard interior point method to crudely detect when the distances from $s$ other vertices $v$ have decreased by a $(1+ε)$ factor, and implementing it using the deterministic min-ratio cycle data structure of [Chen-Kyng-Liu-Meierhans-Probst, STOC 2024].

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Published
2026-06-09
DOI
https://doi.org/10.1145/3798129.3800733
Primary Topic
Advanced Optimization Algorithms Research
Type
article
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article

Incremental Shortest Paths in Almost Linear Time via a Modified Interior Point Method

Yang P. Liu
Advanced Optimization Algorithms Research
article

Incremental Shortest Paths in Almost Linear Time via a Modified Interior Point Method

Yang P. Liu
article en

Abstract

We give an algorithm that takes a directed graph $G$ undergoing $m$ edge insertions with lengths in $[1, W]$, and maintains $(1+ε)$-approximate shortest path distances from a fixed source $s$ to all other vertices. The algorithm is deterministic and runs in total time $m^{1+o(1)}\log W$, for any $ε> \exp(-(\log m)^{0.99})$. This is achieved by designing a nonstandard interior point method to crudely detect when the distances from $s$ other vertices $v$ have decreased by a $(1+ε)$ factor, and implementing it using the deterministic min-ratio cycle data structure of [Chen-Kyng-Liu-Meierhans-Probst, STOC 2024].

Carnegie Mellon University (US)
Openalex Percentile: Top 96%
Advanced Optimization Algorithms Research
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