A Simple Algorithm for Minimum Cuts in Near-Linear Time

We consider the minimum cut problem in undirected, weighted graphs. We give a simple algorithm to find a minimum cut that $2$-respects (cuts two edges of) a spanning tree $T$ of a graph $G$. This procedure can be used in place of the complicated subroutine given in Karger's near-linear time minimum cut algorithm (J. ACM, 2000). We give a self-contained version of Karger's algorithm with the new procedure, which is easy to state and relatively simple to implement. It produces a minimum cut on an $m$-edge, $n$-vertex graph in $O(m \log^3 n)$ time with high probability, matching the complexity of Karger's approach.Comment: To appear in SWAT 202

Weighted Min-Cut: Sequential, Cut-Query and Streaming Algorithms

Consider the following 2-respecting min-cut problem. Given a weighted graph $G$ and its spanning tree $T$, find the minimum cut among the cuts that contain at most two edges in $T$. This problem is an important subroutine in Karger's celebrated randomized near-linear-time min-cut algorithm [STOC'96]. We present a new approach for this problem which can be easily implemented in many settings, leading to the following randomized min-cut algorithms for weighted graphs. * An $O(m\frac{\log^2 n}{\log\log n} + n\log^6 n)$-time sequential algorithm: This improves Karger's $O(m \log^3 n)$ and $O(m\frac{(\log^2 n)\log (n^2/m)}{\log\log n} + n\log^6 n)$ bounds when the input graph is not extremely sparse or dense. Improvements over Karger's bounds were previously known only under a rather strong assumption that the input graph is simple [Henzinger et al. SODA'17; Ghaffari et al. SODA'20]. For unweighted graphs with parallel edges, our bound can be improved to $O(m\frac{\log^{1.5} n}{\log\log n} + n\log^6 n)$. * An algorithm requiring $\tilde O(n)$ cut queries to compute the min-cut of a weighted graph: This answers an open problem by Rubinstein et al. ITCS'18, who obtained a similar bound for simple graphs. * A streaming algorithm that requires $\tilde O(n)$ space and $O(\log n)$ passes to compute the min-cut: The only previous non-trivial exact min-cut algorithm in this setting is the 2-pass $\tilde O(n)$-space algorithm on simple graphs [Rubinstein et al., ITCS'18] (observed by Assadi et al. STOC'19). In contrast to Karger's 2-respecting min-cut algorithm which deploys sophisticated dynamic programming techniques, our approach exploits some cute structural properties so that it only needs to compute the values of $\tilde O(n)$ cuts corresponding to removing $\tilde O(n)$ pairs of tree edges, an operation that can be done quickly in many settings.Comment: Updates on this version: (1) Minor corrections in Section 5.1, 5.2; (2) Reference to newer results by GMW SOSA21 (arXiv:2008.02060v2), DEMN STOC21 (arXiv:2004.09129v2) and LMN 21 (arXiv:2102.06565v1

A sublinear time quantum algorithm for s-t minimum cut on dense simple graphs

An $s{\operatorname{-}}t$ minimum cut in a graph corresponds to a minimum weight subset of edges whose removal disconnects vertices $s$ and $t$. Finding such a cut is a classic problem that is dual to that of finding a maximum flow from $s$ to $t$. In this work we describe a quantum algorithm for the minimum $s{\operatorname{-}}t$ cut problem on undirected graphs. For an undirected graph with $n$ vertices, $m$ edges, and integral edge weights bounded by $W$, the algorithm computes with high probability the weight of a minimum $s{\operatorname{-}}t$ cut in time $\widetilde O(\sqrt{m} n^{5/6} W^{1/3} + n^{5/3} W^{2/3})$, given adjacency list access to $G$. For simple graphs this bound is always $\widetilde O(n^{11/6})$, even in the dense case when $m = \Omega(n^2)$. In contrast, a randomized algorithm must make $\Omega(m)$ queries to the adjacency list of a simple graph $G$ even to decide whether $s$ and $t$ are connected

Finding the KT partition of a weighted graph in near-linear time

In a breakthrough work, Kawarabayashi and Thorup (J.~ACM'19) gave a near-linear time deterministic algorithm for minimum cut in a simple graph $G = (V,E)$. A key component is finding the $(1+\varepsilon)$-KT partition of $G$, the coarsest partition $\{P_1, \ldots, P_k\}$ of $V$ such that for every non-trivial $(1+\varepsilon)$-near minimum cut with sides $\{S, \bar{S}\}$ it holds that $P_i$ is contained in either $S$ or $\bar{S}$, for $i=1, \ldots, k$. Here we give a near-linear time randomized algorithm to find the $(1+\varepsilon)$-KT partition of a weighted graph. Our algorithm is quite different from that of Kawarabayashi and Thorup and builds on Karger's framework of tree-respecting cuts (J.~ACM'00). We describe applications of the algorithm. (i) The algorithm makes progress towards a more efficient algorithm for constructing the polygon representation of the set of near-minimum cuts in a graph. This is a generalization of the cactus representation initially described by Bencz\'ur (FOCS'95). (ii) We improve the time complexity of a recent quantum algorithm for minimum cut in a simple graph in the adjacency list model from $\widetilde O(n^{3/2})$ to $\widetilde O(\sqrt{mn})$. (iii) We describe a new type of randomized algorithm for minimum cut in simple graphs with complexity $O(m + n \log^6 n)$. For slightly dense graphs this matches the complexity of the current best $O(m + n \log^2 n)$ algorithm which uses a different approach based on random contractions. The key technical contribution of our work is the following. Given a weighted graph $G$ with $m$ edges and a spanning tree $T$, consider the graph $H$ whose nodes are the edges of $T$, and where there is an edge between two nodes of $H$ iff the corresponding 2-respecting cut of $T$ is a non-trivial near-minimum cut of $G$. We give a $O(m \log^4 n)$ time deterministic algorithm to compute a spanning forest of $H$

LP-Relaxations for Tree Augmentation

In the Tree Augmentation Problem (TAP) the goal is to augment a tree T by a minimum size edge set F from a given edge set E such that T+F is 2-edge-connected. The best approximation ratio known for TAP is 1.5. In the more general Weighted TAP problem, F should be of minimum weight. Weighted TAP admits several 2-approximation algorithms w.r.t. the standard cut-LP relaxation. The problem is equivalent to the problem of covering a laminar set family. Laminar set families play an important role in the design of approximation algorithms for connectivity network design problems. In fact, Weighted TAP is the simplest connectivity network design problem for which a ratio better than 2 is not known. Improving this "natural" ratio is a major open problem, which may have implications on many other network design problems. It seems that achieving this goal requires finding an LP-relaxation with integrality gap better than 2, which is an old open problem even for TAP. In this paper we introduce two different LP-relaxations, and for each of them give a simple algorithm that computes a feasible solution for TAP of size at most 7/4 times the optimal LP value. This gives some hope to break the ratio 2 for the weighted case

Parameterized Algorithms for Graph Partitioning Problems

In parameterized complexity, a problem instance (I, k) consists of an input I and an extra parameter k. The parameter k usually a positive integer indicating the size of the solution or the structure of the input. A computational problem is called fixed-parameter tractable (FPT) if there is an algorithm for the problem with time complexity O(f(k).nc ), where f(k) is a function dependent only on the input parameter k, n is the size of the input and c is a constant. The existence of such an algorithm means that the problem is tractable for fixed values of the parameter. In this thesis, we provide parameterized algorithms for the following NP-hard graph partitioning problems: (i) Matching Cut Problem: In an undirected graph, a matching cut is a partition of vertices into two non-empty sets such that the edges across the sets induce a matching. The matching cut problem is the problem of deciding whether a given graph has a matching cut. The Matching Cut problem is expressible in monadic second-order logic (MSOL). The MSOL formulation, together with Courcelle’s theorem implies linear time solvability on graphs with bounded tree-width. However, this approach leads to a running time of f(||ϕ||, t) · n, where ||ϕ|| is the length of the MSOL formula, t is the tree-width of the graph and n is the number of vertices of the graph. The dependency of f(||ϕ||, t) on ||ϕ|| can be as bad as a tower of exponentials. In this thesis we give a single exponential algorithm for the Matching Cut problem with tree-width alone as the parameter. The running time of the algorithm is 2O(t) · n. This answers an open question posed by Kratsch and Le [Theoretical Computer Science, 2016]. We also show the fixed parameter tractability of the Matching Cut problem when parameterized by neighborhood diversity or other structural parameters. (ii) H-Free Coloring Problems: In an undirected graph G for a fixed graph H, the H-Free q-Coloring problem asks to color the vertices of the graph G using at most q colors such that none of the color classes contain H as an induced subgraph. That is every color class is H-free. This is a generalization of the classical q-Coloring problem, which is to color the vertices of the graph using at most q colors such that no pair of adjacent vertices are of the same color. The H-Free Chromatic Number is the minimum number of colors required to H-free color the graph. For a fixed q, the H-Free q-Coloring problem is expressible in monadic secondorder logic (MSOL). The MSOL formulation leads to an algorithm with time complexity f(||ϕ||, t) · n, where ||ϕ|| is the length of the MSOL formula, t is the tree-width of the graph and n is the number of vertices of the graph. In this thesis we present the following explicit combinatorial algorithms for H-Free Coloring problems: • An O(q O(t r ) · n) time algorithm for the general H-Free q-Coloring problem, where r = |V (H)|. • An O(2t+r log t · n) time algorithm for Kr-Free 2-Coloring problem, where Kr is a complete graph on r vertices. The above implies an O(t O(t r ) · n log t) time algorithm to compute the H-Free Chromatic Number for graphs with tree-width at most t. Therefore H-Free Chromatic Number is FPT with respect to tree-width. We also address a variant of H-Free q-Coloring problem which we call H-(Subgraph)Free q-Coloring problem, which is to color the vertices of the graph such that none of the color classes contain H as a subgraph (need not be induced). We present the following algorithms for H-(Subgraph)Free q-Coloring problems. • An O(q O(t r ) · n) time algorithm for the general H-(Subgraph)Free q-Coloring problem, which leads to an O(t O(t r ) · n log t) time algorithm to compute the H- (Subgraph)Free Chromatic Number for graphs with tree-width at most t. • An O(2O(t 2 ) · n) time algorithm for C4-(Subgraph)Free 2-Coloring, where C4 is a cycle on 4 vertices. • An O(2O(t r−2 ) · n) time algorithm for {Kr\e}-(Subgraph)Free 2-Coloring, where Kr\e is a graph obtained by removing an edge from Kr. • An O(2O((tr2 ) r−2 ) · n) time algorithm for Cr-(Subgraph)Free 2-Coloring problem, where Cr is a cycle of length r. (iii) Happy Coloring Problems: In a vertex-colored graph, an edge is happy if its endpoints have the same color. Similarly, a vertex is happy if all its incident edges are happy. we consider the algorithmic aspects of the following Maximum Happy Edges (k-MHE) problem: given a partially k-colored graph G, find an extended full k-coloring of G such that the number of happy edges are maximized. When we want to maximize the number of happy vertices, the problem is known as Maximum Happy Vertices (k-MHV). We show that both k-MHE and k-MHV admit polynomial-time algorithms for trees. We show that k-MHE admits a kernel of size k + `, where ` is the natural parameter, the number of happy edges. We show the hardness of k-MHE and k-MHV for some special graphs such as split graphs and bipartite graphs. We show that both k-MHE and k-MHV are tractable for graphs with bounded tree-width and graphs with bounded neighborhood diversity. vii In the last part of the thesis we present an algorithm for the Replacement Paths Problem which is defined as follows: Let G (|V (G)| = n and |E(G)| = m) be an undirected graph with positive edge weights. Let PG(s, t) be a shortest s − t path in G. Let l be the number of edges in PG(s, t). The Edge Replacement Path problem is to compute a shortest s − t path in G\{e}, for every edge e in PG(s, t). The Node Replacement Path problem is to compute a shortest s−t path in G\{v}, for every vertex v in PG(s, t). We present an O(TSP T (G) + m + l 2 ) time and O(m + l 2 ) space algorithm for both the problems, where TSP T (G) is the asymptotic time to compute a single source shortest path tree in G. The proposed algorithm is simple and easy to implement