arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1907.04745cs.DS
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常数时间动态$(Δ+1)$ -最小生成森林的着色和权近似:满足性质测试的动态算法

Constant-Time Dynamic $(Δ+1)$-Coloring and Weight Approximation for Minimum Spanning Forest: Dynamic Algorithms Meet Property Testing

Monika Henzinger, Pan Peng

摘要:

除了少数例外(即,用于最大匹配的算法、$2$ -近似顶点覆盖和某些恒定拉伸的空间),一般图中的所有已知的完全动态算法都需要(摊销的)$Ω(\log n)$更新/查询时间。证明了以下结果:(1)给出了一个具有恒定平均更新时间的全动态(Las-Vegas风格)算法,该算法保持了最大度不超过$Δ$的图的正确$(Δ+1)$ -顶点着色.这改进了Bhattacharya等人先前的$O(\log Δ)$时间算法(SODA 2018)。我们表明,我们的结果不仅有最佳的运行时间,但也是最佳的意义上说,已经决定是否$Δ$着色存在于一个动态变化的图中,最大程度的$Δ$需要$Ω(\log n)$时间每操作。(2)我们给出了两个完全动态的算法,保持$(1+\varepsilon)$ -近似的最小生成森林的图$G$与边的重量在$[1,W]$的重量$M$。我们的确定性算法需要$O({W^2 \log W}/{\varepsilon^3})$最坏情况下的时间,这是恒定的,如果$W$和$\varepsilon$是常数。这是有点令人惊讶的,作为一个较低的边界由帕特拉斯库和Demaine(SIAM J. COMPUT。2006)表明,每次操作都需要$Ω(\log n)$时间来保持MSF的确切重量,即使对于$W=1$也是如此。我们的随机(蒙特卡洛风格)算法的工作概率很高,并运行在最坏情况下的$O(\frac{1}{\varepsilon^4}\log^2(\frac{1}{\varepsilon}))$时间,如果$W= O({(m^*)^{1/3}}/{\log^3 n})$,其中$m^*$是在整个所有更新的图形中的边缘的最小数量。它甚至可以对付适应性强的对手。

英文摘要:

With few exceptions (namely, algorithms for maximal matching, $2$-approximate vertex cover, and certain constant-stretch spanners), all known fully dynamic algorithms in general graphs require (amortized) $Ω(\log n)$ update/query time. Showing for the first time that techniques from property testing can lead to constant-time fully dynamic graph algorithms we prove the following results: (1) We give a fully dynamic (Las-Vegas style) algorithm with constant expected amortized time per update that maintains a proper $(Δ+1)$-vertex coloring of a graph with maximum degree at most $Δ$. This improves upon the previous $O(\log Δ)$-time algorithm by Bhattacharya et al. (SODA 2018). We show that our result does not only have optimal running time, but is also optimal in the sense that already deciding whether a $Δ$-coloring exists in a dynamically changing graph with maximum degree at most $Δ$ takes $Ω(\log n)$ time per operation. (2) We give two fully dynamic algorithms that maintain a $(1+\varepsilon)$-approximation of the weight $M$ of the minimum spanning forest of a graph $G$ with edges weights in $[1,W]$. Our deterministic algorithm takes $O({W^2 \log W}/{\varepsilon^3})$ worst-case time, which is constant if both $W$ and $\varepsilon$ are constant. This is somewhat surprising as a lower bound by Patrascu and Demaine (SIAM J. Comput. 2006) shows that it takes $Ω(\log n)$ time per operation to maintain the exact weight of the MSF that holds even for $W=1$. Our randomized (Monte-Carlo style) algorithm works with high probability and runs in worst-case $O(\frac{1}{\varepsilon^4}\log^2(\frac{1}{\varepsilon}))$ time if $W= O({(m^*)^{1/3}}/{\log^3 n})$, where $m^*$ is the minimum number of edges in the graph throughout all the updates. It works even against an adaptive adversary.

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