In this article, we provide a unified and simplified approach to derandomize central results in the area of fault-tolerant graph algorithms. Given a graph $G$, a vertex pair $(s,t) \in V(G)\times V(G)$, and a set of edge faults $F \subseteq E(G)$, a replacement path $P(s,t,F)$ is an $s$-$t$ shortest path in $G \setminus F$. For integer parameters $L,f$, a replacement path covering (RPC) is a collection of subgraphs of $G$, denoted by $\textit{G}_{L,f}=\{G_1,\ldots, G_r \}$, such that for every set $F$ of at most $f$ faults (i.e., $|F|\le f$) and every replacement path $P(s,t,F)$ of at most $L$ edges, there exists a subgraph $G_i\in \textit{G}_{L,f}$ that contains all the edges of $P$ and does not contain any of the edges of $F$. The covering value of the RPC $\textit{G}_{L,f}$ is then defined to be the number of subgraphs in $\textit{G}_{L,f}$. We present efficient deterministic constructions of $(L,f)$-RPCs whose covering values almost match the randomized ones, for a wide range of parameters. Our time and value bounds improve considerably over the previous construction of Parter (DISC 2019). We also provide an almost matching lower bound for the value of these coverings. A key application of our above deterministic constructions is the derandomization of the algebraic construction of the distance sensitivity oracle by Weimann and Yuster (FOCS 2010). The preprocessing and query time of the our deterministic algorithm nearly match the randomized bounds. This resolves the open problem of Alon, Chechik and Cohen (ICALP 2019).
翻译:在本文中,我们提供了一种统一且简化的方法来去随机化容错图算法领域的核心结果。给定图G,顶点对(s,t)∈V(G)×V(G)和故障边集F⊆E(G),替换路径P(s,t,F)是在G\F中的一条s-t最短路径。对于整数参数L,f,替换路径覆盖(RPC)是一个子图集合(排列),用G(L,f)表示。对于每个最多有f个故障边的集合F(即|F|≤f)和边长度最多为L的每个替换路径P,存在一个子图G_i ∈ G(L,f),其中G_i包含P的所有边且不包含F的任何边。RPC G(L,f)的覆盖值定义为覆盖G(L,f)中的子图数量。我们提供了有效的确定性构造$(L,f)$-RPCs,在广泛的参数范围内其覆盖值与随机化的几乎相匹配。我们的时间和价值界限相对于Parter(DISC 2019)之前的构造有了很大提高。我们还提供了这些覆盖值的几乎匹配下限。我们上述确定性构造的关键应用是解决Weimann和Yuster(FOCS 2010)的距离灵敏度预处理量(DSO)的代数构造去随机化。我们的预处理和查询时间几乎与随机化边界相匹配。这解决了Alon,Chechik和Cohen(ICALP 2019)的悬而未决问题。