← 研踪 PaperTrailarXiv:2609.27797 · v2
Faster Minimum k-Cut II: Improved Randomized and Deterministic Algorithms for Weighted Graphs
首次提交 2026-08-18 01:45
版本更新 2026-11-01 10:44
雷达观测 2026-11-04 23:43
摘要
The Minimum $k$-Cut problem asks for a minimum-weight set of edges whose removal leaves an undirected weighted graph with at least $k$ connected components. We consider only $k\ge3$. The fastest previous algorithm for weighted graphs ran in $n^{k-2}(m+n)(\log n)^{O(k^2)}$ randomized time~\cite{LV26}; on dense graphs this is $\widetilde O_k(n^k)$, and for $k=3$ it gives an $\widetilde O(nm)$-time algorithm. We improve the dense-graph exponent from $k$ to $k-1$ and obtain the same exponent deterministically. After aggregating parallel edges in $O(m)$ time, our randomized algorithm runs with high probability in \begin{equation*} k^{O(k^2)}n^{k-1}\log^2n \end{equation*} time, while our deterministic algorithm runs in \begin{equation*} k^{O(k^2)}n^{k-1}\log^{O(1)}n \end{equation*} time. In particular, weighted Minimum $3$-Cut can be solved in $O(n^2\log^2 n)$ randomized time and in $\widetilde O(n^2)$ deterministic time. The algorithms have two main components. First, we give a faster algorithm for weighted Minimum $3$-Cut. After handling optima with a very small side and optima with two light sides, the remaining optimum has a unique structured side. Tree packing reduces its completion to a batched collection of $2$-respecting cut problems. Second, we reduce Minimum $k$-Cut to Minimum $3$-Cut by enumerating a bounded family of light-cut candidates and recursively completing either side of each candidate. If the enumeration produces too many cuts, then we can instead produce an optimum $k$-cut directly. We derandomize the $3$-cut algorithm using a deterministic near-minimum-cut skeleton, and derandomize the reduction using a specialized $4$-cut algorithm using the skeleton, the constructive light-cut bounds, and the deterministic spectral sparsifier of \cite{BSS12}.
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v2 · 2026-11-01 10:44 · 最新观测版本
Faster Minimum k-Cut II: Improved Randomized and Deterministic Algorithms for Weighted Graphs
The Minimum $k$-Cut problem asks for a minimum-weight set of edges whose removal leaves an undirected weighted graph with at least $k$ connected components. We consider only $k\ge3$. The fastest previous algorithm for weighted graphs ran in $n^{k-2}(m+n)(\log n)^{O(k^2)}$ randomized time~\cite{LV26}; on dense graphs this is $\widetilde O_k(n^k)$, and for $k=3$ it gives an $\widetilde O(nm)$-time algorithm. We improve the dense-graph exponent from $k$ to $k-1$ and obtain the same exponent deterministically. After aggregating parallel edges in $O(m)$ time, our randomized algorithm runs with high probability in \begin{equation*} k^{O(k^2)}n^{k-1}\log^2n \end{equation*} time, while our deterministic algorithm runs in \begin{equation*} k^{O(k^2)}n^{k-1}\log^{O(1)}n \end{equation*} time. In particular, weighted Minimum $3$-Cut can be solved in $O(n^2\log^2 n)$ randomized time and in $\widetilde O(n^2)$ deterministic time. The algorithms have two main components. First, we give a faster algorithm for weighted Minimum $3$-Cut. After handling optima with a very small side and optima with two light sides, the remaining optimum has a unique structured side. Tree packing reduces its completion to a batched collection of $2$-respecting cut problems. Second, we reduce Minimum $k$-Cut to Minimum $3$-Cut by enumerating a bounded family of light-cut candidates and recursively completing either side of each candidate. If the enumeration produces too many cuts, then we can instead produce an optimum $k$-cut directly. We derandomize the $3$-cut algorithm using a deterministic near-minimum-cut skeleton, and derandomize the reduction using a specialized $4$-cut algorithm using the skeleton, the constructive light-cut bounds, and the deterministic spectral sparsifier of \cite{BSS12}.
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