arXiv subjects

Sven O. Krumke

Publications and source records attributed to Sven O. Krumke.

At least 19 recordsLinked to original sources

arXiv2026

A dual view of Roman Domination: The 2-limited packing problem

We consider the 2-limited packing problem: for a graph $G=(V,E)$ one seeks to find a maximum cardinality subset $B\subseteq V$, such that, for all $v\in V$, the closed neighbourhood of $v$ contains at most two vertices in $B$. We compare this packing problem to the well-known Roman domination problem by pointing out some similarities and differences in the behaviour of the optimal solutions of both problems and show that these two problems are weakly dual. We show that for trees, the two problems are strongly dual, letting us solve the Roman domination problem by computing an optimal solution to the 2-limited packing problem.

math.CO↗

arXiv2024

Online Algorithms to Schedule a Proportionate Flexible Flow Shop of Batching Machines

This paper is the first to consider online algorithms to schedule a proportionate flexible flow shop of batching machines (PFFB). The scheduling model is motivated by manufacturing processes of individualized medicaments, which are used in modern medicine to treat some serious illnesses. We provide two different online algorithms, proving also lower bounds for the offline problem to compute their competitive ratios. The first algorithm is an easy-to-implement, general local scheduling heuristic. It is 2-competitive for PFFBs with an arbitrary number of stages and for several natural scheduling objectives. We also show that for total/average flow time, no deterministic algorithm with better competitive ratio exists. For the special case with two stages and the makespan or total completion time objective, we describe an improved algorithm that achieves the best possible competitive ratio $φ=\frac{1+\sqrt{5}}{2}$, the golden ratio. All our results also hold for proportionate (non-flexible) flow shops of batching machines (PFB) for which this is also the first paper to study online algorithms.

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arXiv2024

QCEDA: Using Quantum Computers for EDA

The field of Electronic Design Automation (EDA) is crucial for microelectronics, but the increasing complexity of Integrated Circuits (ICs) poses challenges for conventional EDA: Corresponding problems are often NP-hard and are therefore in general solved by heuristics, not guaranteeing optimal solutions. Quantum computers may offer better solutions due to their potential for optimization through entanglement, superposition, and interference. Most of the works in the area of EDA and quantum computers focus on how to use EDA for building quantum circuits. However, almost no research focuses on exploiting quantum computers for solving EDA problems. Therefore, this paper investigates the feasibility and potential of quantum computing for a typical EDA optimization problem broken down to the Min-$k$-Union problem. The problem is mathematically transformed into a Quadratic Unconstrained Binary Optimization (QUBO) problem, which was successfully solved on an IBM quantum computer and a D-Wave quantum annealer.

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arXiv2022

Almost Disjoint Paths and Separating by Forbidden Pairs

By Menger's theorem the maximum number of arc-disjoint paths from a vertex s to a vertex t in a directed graph equals the minumum number of arcs needed to disconnect s and t, i.e., the minimum size of an s-t-cut. The max-flow problem in a network with unit capacities is equivalent to the arc-disjoint paths problem. Moreover the max-flow and min-cut problems form a strongly dual pair. We relax the disjointedness requirement on the paths, allowing them to be almost disjoint, meaning they may share up to one arc. The resulting almost disjoint paths problem (ADP) asks for k s-t-paths such that any two of them are almost disjoint. The separating by forbidden pairs problem (SFP) is the corresponding dual problem and calls for a set of k arc pairs such that every s-t-path contains both arcs of at least one such pair. In this paper, we explore these two problems, showing that they have an unbounded duality gap in general and analyzing their complexity. We prove that ADP is NP-complete when k is part of the input and that SFP is Sigma_2P-complete, even for acyclic graphs. Furthermore, we efficiently solve ADP when k<=2 is fixed and present a polynomial time algorithm based on dynamic programming for ADP when k is constant and the considered graphs are acyclic.

math.CO↗

arXiv2022

On Reward-Penalty-Selection Games

The Reward-Penalty-Selection Problem (RPSP) can be seen as a combination of the Set Cover Problem (SCP) and the Hitting Set Problem (HSP). Given a set of elements, a set of reward sets, and a set of penalty sets, one tries to find a subset of elements such that as many reward sets as possible are covered, i.e. all elements are contained in the subset, and at the same time as few penalty sets as possible are hit, i.e. the intersection of the subset with the penalty set is non-empty. In this paper we define a cooperative game based on the RPSP where the elements of the RPSP are the players. We prove structural results and show that RPS games are convex, superadditive and totally balanced. Furthermore, the Shapley value can be computed in polynomial time. In addition to that, we provide a characterization of the core elements as a feasible flow in a network graph depending on the instance of the underlying RPSP. By using this characterization, a core element can be computed efficiently.

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arXiv2021

Algorithms and Complexity for the Almost Equal Maximum Flow Problem

In the Equal Maximum Flow Problem (EMFP), we aim for a maximum flow where we require the same flow value on all edges in some given subsets of the edge set. In this paper, we study the closely related Almost Equal Maximum Flow Problems (AEMFP) where the flow values on edges of one homologous edge set differ at most by the valuation of a so called deviation function~$Δ$. We prove that the integer almost equal maximum flow problem (integer AEMFP) is in general $\mathcal{NP}$-complete, and show that even the problem of finding a fractional maximum flow in the case of convex deviation functions is also $\mathcal{NP}$-complete. This is in contrast to the EMFP, which is polynomial time solvable in the fractional case. We provide inapproximability results for the integral AEMFP. For the integer AEMFP we state a polynomial algorithm for the constant deviation and concave case for a fixed number of homologous sets.

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arXiv2020

Simultaneously Dominating all Spanning Trees of a Graph

We investigate the problem of simultaneously dominating all spanning trees of a given graph. We prove that on 2-connected graphs, a subset of the vertices dominates all spanning trees of the graph if and only if it is a vertex cover. Using this fact we present an exact algorithm that finds a simultaneous dominating set of minimum size using an oracle for finding a minimum vertex cover. The algorithm can be implemented to run in polynomial time on several graph classes, such as bipartite or chordal graphs. We prove that there is no polynomial time algorithm that finds a minimum simultaneous dominating set on perfect graphs, unless P=NP. Finally, we provide a 2-approximation algorithm for finding a minimum simultaneous dominating set.

math.CO↗

arXiv2020

Scheduling a Proportionate Flow Shop of Batching Machines

In this paper we study a proportionate flow shop of batching machines with release dates and a fixed number $m \geq 2$ of machines. The scheduling problem has so far barely received any attention in the literature, but recently its importance has increased significantly, due to applications in the industrial scaling of modern bio-medicine production processes. We show that for any fixed number of machines, the makespan and the sum of completion times can be minimized in polynomial time. Furthermore, we show that the obtained algorithm can also be used to minimize the weighted total completion time, maximum lateness, total tardiness and (weighted) number of late jobs in polynomial time if all release dates are $0$. Previously, polynomial time algorithms have only been known for two machines.

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arXiv2020

On the Mixed Connectivity Conjecture of Beineke and Harary

The conjecture of Beineke and Harary states that for any two vertices which can be separated by $k$ vertices and $l$ edges for $l\geq 1$ but neither by $k$ vertices and $l-1$ edges nor $k-1$ vertices and $l$ edges there are $k+l$ edge-disjoint paths connecting these two vertices of which $k+1$ are internally disjoint. In this paper we consider this conjecture for $l=2$ and any $k\in \mathbb{N}$. Afterwards, we utilize this result to prove that the conjecture holds for all graphs of treewidth at most $3$ and all $k$ and $l$. We also show that it is NP-complete to decide whether two vertices can be separated by $k$ vertices and $l$ edges.

math.CO↗

arXiv2020

Robust Single Machine Makespan Scheduling with Release Date Uncertainty

This paper addresses the robust single machine makespan scheduling with uncertain release dates of the jobs. The release dates take values within know intervals. We use the concept of gamma-robustness in two different settings and address both the robust absolute and robust regret criteria. Our main results are polynomial time algorithms which have the same running time (O(n log n)) as the best algorithms for the non-robust case.

math.OC↗

arXiv2020

On the Bicriterion Maximum Flow Network Interdiction Problem

This article focuses on a biobjective extension of the maximum flow network interdiction problem, where each arc in the network is associated with two capacity values. Two maximum flows from a source to a sink are to be computed independently of each other with respect to the first and second capacity function, respectively, while an interdictor aims to minimize the value of both maximum flows by interdicting arcs. We show that this problem is intractable and that the decision problem, which asks whether or not a feasible interdiction strategy is efficient, is NP-complete. We propose a pseudopolynomial time algorithm in the case of two-terminal series-parallel graphs and positive integer-valued interdiction costs. We extend this algorithm to a fully polynomial-time approximation scheme for the case of unit interdiction costs by appropriately partitioning the objective space.

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arXiv2020

Towards obtaining a 3-Decomposition from a perfect Matching

A decomposition of a graph is a set of subgraphs whose edges partition those of $G$. The 3-decomposition conjecture posed by Hoffmann-Ostenhof in 2011 states that every connected cubic graph can be decomposed into a spanning tree, a 2-regular subgraph, and a matching. It has been settled for special classes of graphs, one of the first results being for Hamiltonian graphs. In the past two years several new results have been obtained, adding the classes of plane, claw-free, and 3-connected tree-width 3 graphs to the list. In this paper, we regard a natural extension of Hamiltonian graphs: removing a Hamiltonian cycle from a cubic graph leaves a perfect matching. Conversely, removing a perfect matching $M$ from a cubic graph $G$ leaves a disjoint union of cycles. Contracting these cycles yields a new graph $G_M$. The graph $G$ is star-like if $G_M$ is a star for some perfect matching $M$, making Hamiltonian graphs star-like. We extend the technique used to prove that Hamiltonian graphs satisfy the 3-decomposition conjecture to show that 3-connected star-like graphs satisfy it as well.

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arXiv2018

Robust Multicovers with Budgeted Uncertainty

The Min-$q$-Multiset Multicover problem presented in this paper is a special version of the Multiset Multicover problem. For a fixed positive integer $q$, we are given a finite ground set $J$, an integral demand for each element in $J$ and a collection of subsets of $J$. The task is to choose sets of the collection (multiple choices are allowed) such that each element in $J$ is covered at least as many times as specified by the demand of the element. In contrast to Multiset Multicover, in Min-$q$-Multiset Multicover each of the chosen subsets may only cover up to $q$ of its elements with multiple choices being allowed. Our main focus is a robust version of Min-$q$-Multiset Multicover, called Robust Min-$q$-Multiset Multicover, in which the demand of each element in $J$ may vary in a given interval with an additional budget constraint bounding the sum of the demands. Again, the task is to find a selection of subsets which is feasible for all admissible demands. We show that the non-robust version is NP-complete for $q$ greater than two, whereas the robust version is strongly NP-hard for any positive $q$. Furthermore, we present two solution approaches based on constraint generation and investigate the corresponding separation problems. We present computational results using randomly generated instances as well as instances emerging from the problem of locating emergency doctors.

math.OC↗

arXiv2017

An FPTAS for the Knapsack Problem with Parametric Weights

In this paper, we investigate the parametric weight knapsack problem, in which the item weights are affine functions of the form $w_i(λ) = a_i + λ\cdot b_i$ for $i \in \{1,\ldots,n\}$ depending on a real-valued parameter $λ$. The aim is to provide a solution for all values of the parameter. It is well-known that any exact algorithm for the problem may need to output an exponential number of knapsack solutions. We present the first fully polynomial-time approximation scheme (FPTAS) for the problem that, for any desired precision $\varepsilon \in (0,1)$, computes $(1-\varepsilon)$-approximate solutions for all values of the parameter. Our FPTAS is based on two different approaches and achieves a running time of $\mathcal{O}(n^3/\varepsilon^2 \cdot \min\{ \log^2 P, n^2 \} \cdot \min\{\log M, n \log (n/\varepsilon) / \log(n \log (n/\varepsilon) )\})$ where $P$ is an upper bound on the optimal profit and $M := \max\{W, n \cdot \max\{a_i,b_i: i \in \{1,\ldots,n\}\}\}$ for a knapsack with capacity $W$.

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arXiv2017

An FPTAS for the parametric knapsack problem

In this paper, we investigate the parametric knapsack problem, in which the item profits are affine functions depending on a real-valued parameter. The aim is to provide a solution for all values of the parameter. It is well-known that any exact algorithm for the problem may need to output an exponential number of knapsack solutions. We present a fully polynomial-time approximation scheme (FPTAS) for the problem that, for any desired precision $\varepsilon \in (0,1)$, computes $(1-\varepsilon)$-approximate solutions for all values of the parameter. This is the first FPTAS for the parametric knapsack problem that does not require the slopes and intercepts of the affine functions to be non-negative but works for arbitrary integral values. Our FPTAS outputs $\mathcal{O}(\frac{n^2}{\varepsilon})$ knapsack solutions and runs in strongly polynomial-time $\mathcal{O}(\frac{n^4}{\varepsilon^2})$. Even for the special case of positive input data, this is the first FPTAS with a strongly polynomial running time. We also show that this time bound can be further improved to $\mathcal{O}(\frac{n^2}{\varepsilon} \cdot A(n,\varepsilon))$, where $A(n,\varepsilon)$ denotes the running time of any FPTAS for the traditional (non-parametric) knapsack problem.

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arXiv2017

Minimum Cycle Decomposition: A Constructive Characterization for Graphs of Treewidth Two with Node Degrees Two and Four

Substantial efforts have been made to compute or estimate the minimum number $c(G)$ of cycles needed to partition the edges of an Eulerian graph. We give an equivalent characterization of Eulerian graphs of treewidth $2$ and with maximum degree $4$. This characterization enables us to present a linear time algorithm for the computation of $c(G)$ for all $G$ in this class.

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arXiv2016

A Generalized Approximation Framework for Fractional Network Flow and Packing Problems

We generalize the fractional packing framework of Garg and Koenemann to the case of linear fractional packing problems over polyhedral cones. More precisely, we provide approximation algorithms for problems of the form $\max\{c^T x : Ax \leq b, x \in C \}$, where the matrix $A$ contains no negative entries and $C$ is a cone that is generated by a finite set $S$ of non-negative vectors. While the cone is allowed to require an exponential-sized representation, we assume that we can access it via one of three types of oracles. For each of these oracles, we present positive results for the approximability of the packing problem. In contrast to other frameworks, the presented one allows the use of arbitrary linear objective functions and can be applied to a large class of packing problems without much effort. In particular, our framework instantly allows to derive fast and simple fully polynomial-time approximation algorithms (FPTASs) for a large set of network flow problems, such as budget-constrained versions of traditional network flows, multicommodity flows, or generalized flows. Some of these FPTASs represent the first ones of their kind, while others match existing results but offer a much simpler proof.

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arXiv2016

A Network Simplex Method for the Budget-Constrained Minimum Cost Flow Problem

We present a specialized network simplex algorithm for the budget-constrained minimum cost flow problem, which is an extension of the traditional minimum cost flow problem by a second kind of costs associated with each edge, whose total value in a feasible flow is constrained by a given budget B. We present a fully combinatorial description of the algorithm that is based on a novel incorporation of two kinds of integral node potentials and three kinds of reduced costs. We prove optimality criteria and combine two methods that are commonly used to avoid cycling in traditional network simplex algorithms into new techniques that are applicable to our problem. With these techniques and our definition of the reduced costs, we are able to prove a pseudo-polynomial running time of the overall procedure, which can be further improved by incorporating Dantzig's pivoting rule. Moreover, we present computational results that compare our procedure with Gurobi.

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