arXiv subjects

Naoki Katoh

Publications and source records attributed to Naoki Katoh.

At least 19 recordsLinked to original sources

arXiv2023

The Two-Squirrel Problem and Its Relatives

In this paper, we start with a variation of the star cover problem called the Two-Squirrel problem. Given a set $P$ of $2n$ points in the plane, and two sites $c_1$ and $c_2$, compute two $n$-stars $S_1$ and $S_2$ centered at $c_1$ and $c_2$ respectively such that the maximum weight of $S_1$ and $S_2$ is minimized. This problem is strongly NP-hard by a reduction from Equal-size Set-Partition with Rationals. Then we consider two variations of the Two-Squirrel problem, namely the Two-MST and Two-TSP problem, which are both NP-hard. The NP-hardness for the latter is obvious while the former needs a non-trivial reduction from Equal-size Set-Partition with Rationals. In terms of approximation algorithms, for Two-MST and Two-TSP we give factor 3.6402 and $4+\varepsilon$ approximations respectively. Finally, we also show some interesting polynomial-time solvable cases for Two-MST.

cs.CG↗

arXiv2023

Faster Algorithms for Evacuation Problems in Networks with the Single Sink of Small Degree

In this paper, we propose new algorithms for evacuation problems defined on dynamic flow networks. A dynamic flow network is a directed graph in which source nodes are given supplies (i.e., the number of evacuees) and a single sink node is given a demand (i.e., the maximum number of acceptable evacuees). The evacuation problem seeks a dynamic flow that sends all supplies from sources to the sink such that its demand is satisfied in the minimum feasible time horizon. For this problem, the current best algorithms are developed by Schl\"oter (2018) and Kamiyama (2019), which run in strongly polynomial time but with highorder polynomial time complexity because they use submodular function minimization as a subroutine. In this paper, we propose new algorithms that do not explicitly execute submodular function minimization, and we prove that they are faster than those by Schl\"oter (2018) and Kamiyama (2019) when an input network is restricted such that the sink has a small in-degree and every edge has the same capacity.

cs.DS↗

arXiv2020

Minmax Regret 1-Sink Location Problems on Dynamic Flow Path Networks with Parametric Weights

This paper addresses the minmax regret 1-sink location problem on dynamic flow path networks with parametric weights. We are given a dynamic flow network consisting of an undirected path with positive edge lengths, positive edge capacities, and nonnegative vertex weights. A path can be considered as a road, an edge length as the distance along the road and a vertex weight as the number of people at the site. An edge capacity limits the number of people that can enter the edge per unit time. We consider the problem of locating a sink in the network, to which all the people evacuate from the vertices as quickly as possible. In our model, each weight is represented by a linear function in a common parameter $t$, and the decision maker who determines the location of a sink does not know the value of $t$. We formulate the sink location problem under such uncertainty as the minmax regret problem. Given $t$ and a sink location $x$, the cost of $x$ under $t$ is the sum of arrival times at $x$ for all the people determined by $t$. The regret for $x$ under $t$ is the gap between the cost of $x$ under $t$ and the optimal cost under $t$. The task of the problem is formulated as the one to find a sink location that minimizes the maximum regret over all $t$. For the problem, we propose an $O(n^4 2^{α(n)} α(n) \log n)$ time algorithm where $n$ is the number of vertices in the network and $α(\cdot)$ is the inverse Ackermann function. Also for the special case in which every edge has the same capacity, we show that the complexity can be reduced to $O(n^3 2^{α(n)} α(n) \log n)$.

cs.DS↗

arXiv2020

Almost Linear Time Algorithms for Minsum $k$-Sink Problems on Dynamic Flow Path Networks

We address the facility location problems on dynamic flow path networks. A dynamic flow path network consists of an undirected path with positive edge lengths, positive edge capacities, and positive vertex weights. A path can be considered as a road, an edge length as the distance along the road and a vertex weight as the number of people at the site. An edge capacity limits the number of people that can enter the edge per unit time. In the dynamic flow network, given particular points on edges or vertices, called sinks, all the people evacuate from the vertices to the sinks as quickly as possible. The problem is to find the location of sinks on a dynamic flow path network in such a way that the aggregate evacuation time (i.e., the sum of evacuation times for all the people) to sinks is minimized. We consider two models of the problem: the confluent flow model and the non-confluent flow model. In the former model, the way of evacuation is restricted so that all the people at a vertex have to evacuate to the same sink, and in the latter model, there is no such restriction. In this paper, for both the models, we develop algorithms which run in almost linear time regardless of the number of sinks. It should be stressed that for the confluent flow model, our algorithm improves upon the previous result by Benkoczi et al. [Theoretical Computer Science, 2020], and one for the non-confluent flow model is the first polynomial time algorithm.

cs.DS↗

arXiv2020

On Computing a Center Persistence Diagram

Throughout this paper, a persistence diagram ${\cal P}$ is composed of a set $P$ of planar points (each corresponding to a topological feature) above the line $Y=X$, as well as the line $Y=X$ itself, i.e., ${\cal P}=P\cup\{(x,y)|y=x\}$. Given a set of persistence diagrams ${\cal P}_1,...,{\cal P}_m$, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} ${\cal C}$ of them first under the bottleneck distance. We consider two discrete versions and one continuous version. For technical reasons, we first focus on the case when $|P_i|$'s are all the same (i.e., all have the same size $n$), and the problem is to compute a center point set $C$ under the bottleneck matching distance. We show, by a non-trivial reduction from the Planar 3D-Matching problem, that this problem is NP-hard even when $m=3$ diagrams are given. This implies that the general center problem for persistence diagrams under the bottleneck distance, when $P_i$'s possibly have different sizes, is also NP-hard when $m\geq 3$. On the positive side, we show that this problem is polynomially solvable when $m=2$ and admits a factor-2 approximation for $m\geq 3$. These positive results hold for any $L_p$ metric when $P_i$'s are point sets of the same size, and also hold for the case when $P_i$'s have different sizes in the $L_\infty$ metric (i.e., for the Center Persistence Diagram problem). This is the best possible in polynomial time for the Center Persistence Diagram under the bottleneck distance unless P = NP. All these results hold for both of the discrete versions as well as the continuous version; in fact, the NP-hardness and approximation results also hold under the Wasserstein distance for the continuous version.

cs.CG↗

arXiv2018

Minsum $k$-Sink Problem on Path Networks

We consider the problem of locating a set of $k$ sinks on a path network with general edge capacities that minimizes the sum of the evacuation times of all evacuees. We first present an $O(kn\log^4n)$ time algorithm when the edge capacities are non-uniform, where $n$ is the number of vertices. We then present an $O(kn\log^3 n)$ time algorithm when the edge capacities are uniform. We also present an $O(n\log n)$ time algorithm for the special case where $k=1$ and the edge capacities are non-uniform.

cs.DS↗

arXiv2018

Minmax Regret 1-Sink for Aggregate Evacuation Time on Path Networks

Evacuation in emergency situations can be modeled by a dynamic flow network. Two criteria have been used before: one is the evacuation completion time and the other is the aggregate evacuation time of individual evacuees. The aim of this paper is to optimize the aggregate evacuation time in the simplest case, where the network is a path and only one evacuation center (called a sink) is to be introduced. The evacuees are initially located at the vertices, but their precise numbers are unknown, and are given by upper and lower bounds. Under this assumption, we compute the sink location that minimizes the maximum "regret." We present an $O(n^2\log n)$ time algorithm to solve this problem, improving upon the previously fastest $O(n^3)$ time algorithm, where $n$ is the number of vertices.

cs.DS↗

arXiv2016

Improved Algorithms for Computing $k$-Sink on Dynamic Path Networks

We present a novel approach to finding the $k$-sink on dynamic path networks with general edge capacities. Our first algorithm runs in $O(n \log n + k^2 \log^4 n)$ time, where $n$ is the number of vertices on the given path, and our second algorithm runs in $O(n \log^3 n)$ time. Together, they improve upon the previously most efficient $O(kn \log^2 n)$ time algorithm due to Arumugam et al. for all values of $k$. In the case where all the edges have the same capacity, we again present two algorithms that run in $O(n + k^2 \log^2n)$ time and $O(n \log n)$ time, respectively, and they together improve upon the previously best $O(kn)$ time algorithm due to Higashikawa et al. for all values of $k$.

cs.DS↗

arXiv2016

Optimal Evacuation Flows on Dynamic Paths with General Edge Capacities

A Dynamic Graph Network is a graph in which each edge has an associated travel time and a capacity (width) that limits the number of items that can travel in parallel along that edge. Each vertex in this dynamic graph network begins with the number of items that must be evacuated into designated sink vertices. A $k$-sink evacuation protocol finds the location of $k$ sinks and associated evacuation movement protocol that allows evacuating all the items to a sink in minimum time. The associated evacuation movement must impose a confluent flow, i.e, all items passing through a particular vertex exit that vertex using the same edge. In this paper we address the $k$-sink evacuation problem on a dynamic path network. We provide solutions that run in $O(n \log n)$ time for $k=1$ and $O(k n \log^2 n)$ for $k >1$ and work for arbitrary edge capacities.

cs.DS↗

arXiv2016

Geometric k-Center Problems with Centers Constrained to Two Lines

We consider the $k$-center problem in which the centers are constrained to lie on two lines. Given a set of $n$ weighted points in the plane, we want to locate up to $k$ centers on two parallel lines. We present an $O(n\log^2 n)$ time algorithm, which minimizes the weighted distance from any point to a center. We then consider the unweighted case, where the centers are constrained to be on two perpendicular lines. Our algorithms run in $O(n\log^2 n)$ time also in this case.

cs.CG↗

arXiv2015

Minimax Regret 1-Median Problem in Dynamic Path Networks

This paper considers the minimax regret 1-median problem in dynamic path networks. In our model, we are given a dynamic path network consisting of an undirected path with positive edge lengths, uniform positive edge capacity, and nonnegative vertex supplies. Here, each vertex supply is unknown but only an interval of supply is known. A particular assignment of supply to each vertex is called a scenario. Given a scenario s and a sink location x in a dynamic path network, let us consider the evacuation time to x of a unit supply given on a vertex by s. The cost of x under s is defined as the sum of evacuation times to x for all supplies given by s, and the median under s is defined as a sink location which minimizes this cost. The regret for x under s is defined as the cost of x under s minus the cost of the median under s. Then, the problem is to find a sink location such that the maximum regret for all possible scenarios is minimized. We propose an O(n^3) time algorithm for the minimax regret 1-median problem in dynamic path networks with uniform capacity, where n is the number of vertices in the network.

cs.DS↗

arXiv2015

Straight-line Drawability of a Planar Graph Plus an Edge

We investigate straight-line drawings of topological graphs that consist of a planar graph plus one edge, also called almost-planar graphs. We present a characterization of such graphs that admit a straight-line drawing. The characterization enables a linear-time testing algorithm to determine whether an almost-planar graph admits a straight-line drawing, and a linear-time drawing algorithm that constructs such a drawing, if it exists. We also show that some almost-planar graphs require exponential area for a straight-line drawing.

cs.CG↗

arXiv2015

Polynomial-time approximability of the k-Sink Location problem

A dynamic network ${\cal N} = (G,c,τ,S)$ where $G=(V,E)$ is a graph, integers $τ(e)$ and $c(e)$ represent, for each edge $e\in E$, the time required to traverse edge $e$ and its nonnegative capacity, and the set $S\subseteq V$ is a set of sources. In the $k$-{\sc Sink Location} problem, one is given as input a dynamic network ${\cal N}$ where every source $u\in S$ is given a nonnegative supply value $σ(u)$. The task is then to find a set of sinks $X = \{x_1,\ldots,x_k\}$ in $G$ that minimizes the routing time of all supply to $X$. Note that, in the case where $G$ is an undirected graph, the optimal position of the sinks in $X$ needs not be at vertices, and can be located along edges. Hoppe and Tardos showed that, given an instance of $k$-{\sc Sink Location} and a set of $k$ vertices $X\subseteq V$, one can find an optimal routing scheme of all the supply in $G$ to $X$ in polynomial time, in the case where graph $G$ is directed. Note that when $G$ is directed, this suffices to obtain polynomial-time solvability of the $k$-{\sc Sink Location} problem, since any optimal position will be located at vertices of $G$. However, the computational complexity of the $k$-{\sc Sink Location} problem on general undirected graphs is still open. In this paper, we show that the $k$-{\sc Sink Location} problem admits a fully polynomial-time approximation scheme (FPTAS) for every fixed $k$, and that the problem is $W[1]$-hard when parameterized by $k$.

cs.DS↗

arXiv2014

Improved Algorithms for Multiple Sink Location Problems in Dynamic Path Networks

This paper considers the k-sink location problem in dynamic path networks. In our model, a dynamic path network consists of an undirected path with positive edge lengths, uniform edge capacity, and positive vertex supplies. Here, each vertex supply corresponds to a set of evacuees. Then, the problem requires to find the optimal location of $k$ sinks in a given path so that each evacuee is sent to one of k sinks. Let x denote a k-sink location. Under the optimal evacuation for a given x, there exists a (k-1)-dimensional vector d, called (k-1)-divider, such that each component represents the boundary dividing all evacuees between adjacent two sinks into two groups, i.e., all supplies in one group evacuate to the left sink and all supplies in the other group evacuate to the right sink. Therefore, the goal is to find x and d which minimize the maximum cost or the total cost, which are denoted by the minimax problem and the minisum problem, respectively. We study the k-sink location problem in dynamic path networks with continuous model, and prove that the minimax problem can be solved in O(kn) time and the minisum problem can be solved in O(n^2 min{k, 2^{sqrt{log k log log n}}}) time, where n is the number of vertices in the given network. Note that these improve the previous results by [6].

cs.DS↗

arXiv2014

Extended Formulations for Sparsity Matroids

We show the existence of a polynomial-size extended formulation for the base polytope of a $(k,\ell)$-sparsity matroid. For an undirected graph $G=(V,E)$, the size of the formulation is $O(|V||E|)$ when $k \geq \ell$ and $O(|V|^2 |E|)$ when $k \leq \ell$. To this end, we employ the technique developed by Faenza et al. recently that uses a randomized communication protocol.

math.CO↗

arXiv2011

Rooted-tree Decompositions with Matroid Constraints and the Infinitesimal Rigidity of Frameworks with Boundaries

As an extension of a classical tree-partition problem, we consider decompositions of graphs into edge-disjoint (rooted-)trees with an additional matroid constraint. Specifically, suppose we are given a graph $G=(V,E)$, a multiset $R=\{r1,..., r_t\}$ of vertices in $V$, and a matroid ${\cal M}$ on $R$. We prove a necessary and sufficient condition for $G$ to be decomposed into $t$ edge-disjoint subgraphs $G_1=(V_1,T_1),..., G_t=(V_t,T_t)$ such that (i) for each $i$, $G_i$ is a tree with $r_i\in V_i$, and (ii) for each $v\in V$, the multiset $\{r_i\in R\mid v\in V_i\}$ is a base of ${\cal M}$. If ${\cal M}$ is a free matroid, this is a decomposition into $t$ edge-disjoint spanning trees; thus, our result is a proper extension of Nash-Williams' tree-partition theorem. Such a matroid constraint is motivated by combinatorial rigidity theory. As a direct application of our decomposition theorem, we present characterizations of the infinitesimal rigidity of frameworks with non-generic "boundary", which extend classical Laman's theorem for generic 2-rigidity of bar-joint frameworks and Tay's theorem for generic $d$-rigidity of body-bar frameworks.

math.CO↗

arXiv2009

A Proof of the Molecular Conjecture

A $d$-dimensional body-and-hinge framework is a structure consisting of rigid bodies connected by hinges in $d$-dimensional space. The generic infinitesimal rigidity of a body-and-hinge framework has been characterized in terms of the underlying multigraph independently by Tay and Whiteley as follows: A multigraph $G$ can be realized as an infinitesimally rigid body-and-hinge framework by mapping each vertex to a body and each edge to a hinge if and only if ({d+1 \choose 2}-1)G$ contains ${d+1\choose 2}$ edge-disjoint spanning trees, where $({d+1 \choose 2}-1)G$ is the graph obtained from $G$ by replacing each edge by $({d+1\choose 2}-1)$ parallel edges. In 1984 they jointly posed a question about whether their combinatorial characterization can be further applied to a nongeneric case. Specifically, they conjectured that $G$ can be realized as an infinitesimally rigid body-and-hinge framework if and only if $G$ can be realized as that with the additional ``hinge-coplanar'' property, i.e., all the hinges incident to each body are contained in a common hyperplane. This conjecture is called the Molecular Conjecture due to the equivalence between the infinitesimal rigidity of ``hinge-coplanar'' body-and-hinge frameworks and that of bar-and-joint frameworks derived from molecules in 3-dimension. In 2-dimensional case this conjecture has been proved by Jackson and Jord{á}n in 2006. In this paper we prove this long standing conjecture affirmatively for general dimension.

math.CO↗

arXiv2008

Covering Directed Graphs by In-trees

Given a directed graph $D=(V,A)$ with a set of $d$ specified vertices $S=\{s_1,...,s_d\}\subseteq V$ and a function $f\colon S \to \mathbb{Z}_+$ where $\mathbb{Z}_+$ denotes the set of non-negative integers, we consider the problem which asks whether there exist $\sum_{i=1}^d f(s_i)$ in-trees denoted by $T_{i,1},T_{i,2},..., T_{i,f(s_i)}$ for every $i=1,...,d$ such that $T_{i,1},...,T_{i,f(s_i)}$ are rooted at $s_i$, each $T_{i,j}$ spans vertices from which $s_i$ is reachable and the union of all arc sets of $T_{i,j}$ for $i=1,...,d$ and $j=1,...,f(s_i)$ covers $A$. In this paper, we prove that such set of in-trees covering $A$ can be found by using an algorithm for the weighted matroid intersection problem in time bounded by a polynomial in $\sum_{i=1}^df(s_i)$ and the size of $D$. Furthermore, for the case where $D$ is acyclic, we present another characterization of the existence of in-trees covering $A$, and then we prove that in-trees covering $A$ can be computed more efficiently than the general case by finding maximum matchings in a series of bipartite graphs.

cs.DM↗