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Fabian Frei

Publications and source records attributed to Fabian Frei.

At least 19 recordsLinked to original sources

Overlapping Unfoldings of Cones and Convex Polyhedra

Research on D\"urer's problem focuses on edge unfoldings of convex polyhedra that avoid overlap. We invert the goal and find unfoldings that overlap at some point to any given thickness t. We have two main results. The first is that, if we allow unfolding cuts that do not follow polyhedron edges, then there is a convex polyhedron that can unfold with overlap of any given thickness. The second result is that for any given thickness, there is a convex polyhedron with an edge unfolding that overlaps to that thickness.

cs.CG

Time-Optimal $k$-Server

The time-optimal $k$-server problem minimizes the time spent serving all requests instead of the distances traveled. We give a lower bound of $2k-1$ on the competitive ratio of any deterministic online algorithm for this problem, which coincides with the best known upper bound on the competitive ratio achieved by the work-function algorithm for the classical $k$-server problem. We provide further lower bounds of $k+1$ for all Euclidean spaces and $k$ for uniform metric spaces. For the latter, we give a matching $k$-competitive deterministic algorithm. Our most technical result, proven by applying Yao's principle to a suitable instance distribution on a specifically constructed metric space, is a lower bound of $k+\mathcal{O}(\log k)$ that holds even for randomized algorithms, which contrasts with the best known lower bound for the classical problem that remains polylogarithmic. With this paper, we hope to initiate a further study of this natural yet neglected problem.

cs.DS

From Chinese Postman to Salesman and Beyond II: Inapproximability and Parameterized Complexity

A well-studied continuous model of graphs considers each edge as a continuous unit-length interval of points. In the problem $\delta$-Tour defined within this model, the objective to find a shortest tour that comes within a distance of $\delta$ of every point on every edge. This parameterized problem was introduced in the predecessor to this article and shown to be essentially equivalent to the Chinese Postman problem for $\delta = 0$, to the graphic Travel Salesman Problem (TSP) for $\delta = 1/2$, and close to first Vertex Cover and then Dominating Set for even larger $\delta$. Moreover, approximation algorithms for multiple parameter ranges were provided. In this article, we provide complementing inapproximability bounds and examine the fixed-parameter tractability of the problem. On the one hand, we show the following: (1) For every fixed $0 < \delta < 3/2$, the problem $\delta$-Tour is APX-hard, while for every fixed $\delta \geq 3/2$, the problem has no polynomial-time $o(\log{n})$-approximation unless P = NP. Our techniques also yield the new result that TSP remains APX-hard on cubic (and even cubic bipartite) graphs. (2) For every fixed $0 < \delta < 3/2$, the problem $\delta$-Tour is fixed-parameter tractable (FPT) when parameterized by the length of a shortest tour, while it is W[2]-hard for every fixed $\delta \geq 3/2$ and para-NP-hard for $\delta$ being part of the input. On the other hand, if $\delta$ is considered to be part of the input, then an interesting nontrivial phenomenon occurs when $\delta$ is a constant fraction of the number of vertices: (3) If $\delta$ is part of the input, then the problem can be solved in time $f(k)n^{O(k)}$, where $k = \lceil n/\delta \rceil$; however, assuming the Exponential-Time Hypothesis (ETH), there is no algorithm that solves the problem and runs in time $f(k)n^{o(k/\log k)}$.

cs.DS

Fine-Grained Complexity of Ambiguity Problems on Automata and Directed Graphs

In the field of computational logic, two classes of finite automata are considered fundamental: deterministic and nondeterministic automata (DFAs and NFAs). In a more fine-grained approach three natural intermediate classes were introduced, defined by restricting the number of accepting runs of the input NFA. The classes are called: unambiguous, finitely ambiguous, and polynomially ambiguous finite automata. It was observed that central problems, like equivalence, become tractable when the input NFA is restricted to some of these classes. This naturally brought interest into problems determining whether an input NFA belongs to the intermediate classes. Our first result is a nearly complete characterization of the fine-grained complexity of these problems. We show that the respective quadratic and cubic running times of Allauzen et al. are optimal under the Orthogonal Vectors hypothesis or the k-Cycle hypothesis, for alphabets with at least two symbols. In contrast, for unary alphabets we show that all aforementioned variants of ambiguity can be decided in almost linear time. Finally, we study determinisability of unambiguous weighted automata. We positively resolve a conjecture of Allauzen and Mohri, proving that their quadratic-time algorithm for verifying determinisability of unambiguous weighted automata is optimal, assuming the Orthogonal Vectors hypothesis or the k-Cycle hypothesis. We additionally show that for unary alphabets, this can be decided in linear time.

cs.FL

From Chinese Postman to Salesman and Beyond I: Approximating Shortest Tours $\delta$-Covering All Points on All Edges

A well-studied continuous model of graphs, introduced by Dearing and Francis [Transportation Science, 1974], considers each edge as a continuous unit-length interval of points. For $\delta \geq 0$, we introduce the problem $\delta$-Tour, where the objective is to find the shortest tour that comes within a distance of $\delta$ of every point on every edge. It can be observed that 0-Tour is essentially equivalent to the Chinese Postman Problem, which is solvable in polynomial time. In contrast, 1/2-Tour is essentially equivalent to the Graphic Traveling Salesman Problem (TSP), which is NP-hard but admits a constant-factor approximation in polynomial time. We investigate $\delta$-Tour for other values of $\delta$, noting that the problem's behavior and the insights required to understand it differ significantly across various $\delta$ regimes. We design polynomial-time approximation algorithms summarized as follows: (1) For every fixed $0 < \delta < 3/2$, the problem $\delta$-Tour admits a constant-factor approximation. (2) For every fixed $\delta \geq 3/2$, the problem admits an $O(\log{n})$-approximation. (3) If $\delta$ is considered to be part of the input, then the problem admits an $O(\log^3{n})$-approximation. This is the first of two articles on the $\delta$-Tour problem. In the second one we complement the approximation algorithms presented here with inapproximability results and related to parameterized complexity.

cs.DS

Gathering Semi-Synchronously Scheduled Two-State Robots

We study the problem \emph{Gathering} for $n$ autonomous mobile robots in synchronous settings with a persistent memory called \emph{light}. It is well known that Gathering is impossible in the basic model ($OBLOT$) where robots have no lights, even if the system is semi-synchronous (called SSYNCH). Gathering becomes possible, however, if each robot has a light of some type that can be set to a constant number of colors. In the $FCOM$ model, the robots can only see the lights of other robots. In the $FSTA$ model, each robot can only observe its own light. In the $LUMI$ model, all robots can see all lights. This paper focuses on $FSTA$ robots with 2-colored lights in synchronous settings. We show that 2-color $FSTA$ and $FCOM$ robots cannot solve Gathering in SSYNCH without additional conditions, even with rigid movement and agreement of chirality and the minimum moving distance. We also improve the condition of the previous gathering algorithm for $FSTA$ robots with 2-color working in SSYNCH.

cs.DC

Tree Coloring: Random Order and Predictions

Coloring is a notoriously hard problem, and even more so in the online setting, where each arriving vertex has to be colored immediately and irrevocably. Already on trees, which are trivially two-colorable, it is impossible to achieve anything better than a logarithmic competitive ratio. We show how to undercut this bound by a double-logarithmic factor in the slightly relaxed online model where the vertices arrive in random order. We then also analyze algorithms with predictions, showing how well we can color trees with machine-learned advice of varying reliability. We further extend our analysis to all two-colorable graphs and provide matching lower bounds in both cases. Finally, we demonstrate how the two mentioned approaches, both of which diminish the often unjustified pessimism of the classical online model, can be combined to yield even better results.

cs.DS

Content-Oblivious Leader Election on Rings

In content-oblivious computation, n nodes wish to compute a given task over an asynchronous network that suffers from an extremely harsh type of noise, which corrupts the content of all messages across all channels. In a recent work, Censor-Hillel, Cohen, Gelles, and Sela (Distributed Computing, 2023) showed how to perform arbitrary computations in a content-oblivious way in 2-edge connected networks but only if the network has a distinguished node (called root) to initiate the computation. Our goal is to remove this assumption, which was conjectured to be necessary. Achieving this goal essentially reduces to performing a content-oblivious leader election since an elected leader can then serve as the root required to perform arbitrary content-oblivious computations. We focus on ring networks, which are the simplest 2-edge connected graphs. On oriented rings, we obtain a leader election algorithm with message complexity O(n*ID_max), where ID_max is the maximal assigned ID. As it turns out, this dependency on $ID_max$ is inherent: we show a lower bound of Omega(n*log(ID_max/n)) messages for content-oblivious leader election algorithms. We also extend our results to non-oriented rings, where nodes cannot tell which channel leads to which neighbor. In this case, however, the algorithm does not terminate but only reaches quiescence.

cs.DS

Removable Online Knapsack and Advice

In the knapsack problem, we are given a knapsack of some capacity and a set of items, each with a size and a value. The goal is to pack a selection of these items fitting the knapsack that maximizes the total value. The online version of this problem reveals the items one by one. For each item, the algorithm must decide immediately whether to pack it or not. We consider a natural variant of this problem, coined removable online knapsack. It differs from the classical variant by allowing the removal of packed items. Repacking is impossible, however: Once an item is removed, it is gone for good. We analyze the advice complexity of this problem. It measures how many advice bits an omniscient oracle needs to provide for an online algorithm to reach any given competitive ratio, which is, understood in its strict sense, just the approximation factor. We show that the competitive ratio jumps from unbounded without advice to near-optimal with just constantly many advice bits, a behavior unique among all problems examined so far. We also examine algorithms with barely any advice, for example just a single bit, and analyze the special case of the proportional knapsack problem, where an item's size always equals its value. We show that advice algorithms have various concrete applications and that lower bounds on the advice complexity of any problem are exceptionally strong. Our results improve some of the best known lower bounds on the competitive ratio for randomized algorithms and even for deterministic deterministic algorithms in established models such as knapsack with a resource buffer and various problems with multiple knapsacks. The seminal paper introducing knapsack with removability proposed such a problem for which we can even establish a one-to-one correspondence with the advice model; this paper therefore also provides a comprehensive analysis for this neglected problem.

cs.DS

Hitting Meets Packing: How Hard Can it Be?

We study a general family of problems that form a common generalization of classic hitting (also referred to as covering or transversal) and packing problems. An instance of X-HitPack asks: Can removing k (deletable) vertices of a graph G prevent us from packing $\ell$ vertex-disjoint objects of type X? This problem captures a spectrum of problems with standard hitting and packing on opposite ends. Our main motivating question is whether the combination X-HitPack can be significantly harder than these two base problems. Already for a particular choice of X, this question can be posed for many different complexity notions, leading to a large, so-far unexplored domain in the intersection of the areas of hitting and packing problems. On a high-level, we present two case studies: (1) X being all cycles, and (2) X being all copies of a fixed graph H. In each, we explore the classical complexity, as well as the parameterized complexity with the natural parameters k+l and treewidth. We observe that the combined problem can be drastically harder than the base problems: for cycles or for H being a connected graph with at least 3 vertices, the problem is Σ_2^P-complete and requires double-exponential dependence on the treewidth of the graph (assuming the Exponential-Time Hypothesis). In contrast, the combined problem admits qualitatively similar running times as the base problems in some cases, although significant novel ideas are required. For example, for X being all cycles, we establish a 2^poly(k+l)n^O(1) algorithm using an involved branching method. Also, for X being all edges (i.e., H = K_2; this combines Vertex Cover and Maximum Matching) the problem can be solved in time 2^\poly(tw)n^O(1) on graphs of treewidth tw. The key step enabling this running time relies on a combinatorial bound obtained from an algebraic (linear delta-matroid) representation of possible matchings.

cs.DS

Delaying Decisions and Reservation Costs

We study the Feedback Vertex Set and the Vertex Cover problem in a natural variant of the classical online model that allows for delayed decisions and reservations. Both problems can be characterized by an obstruction set of subgraphs that the online graph needs to avoid. In the case of the Vertex Cover problem, the obstruction set consists of an edge (i.e., the graph of two adjacent vertices), while for the Feedback Vertex Set problem, the obstruction set contains all cycles. In the delayed-decision model, an algorithm needs to maintain a valid partial solution after every request, thus allowing it to postpone decisions until the current partial solution is no longer valid for the current request. The reservation model grants an online algorithm the new and additional option to pay a so-called reservation cost for any given element in order to delay the decision of adding or rejecting it until the end of the instance. For the Feedback Vertex Set problem, we first analyze the variant with only delayed decisions, proving a lower bound of $4$ and an upper bound of $5$ on the competitive ratio. Then we look at the variant with both delayed decisions and reservation. We show that given bounds on the competitive ratio of a problem with delayed decisions impliy lower and upper bounds for the same problem when adding the option of reservations. This observation allows us to give a lower bound of $\min{\{1+3α,4\}}$ and an upper bound of $\min{\{1+5α,5\}}$ for the Feedback Vertex Set problem. Finally, we show that the online Vertex Cover problem, when both delayed decisions and reservations are allowed, is $\min{\{1+2α, 2\}}$-competitive, where $α\in \mathbb{R}_{\geq 0}$ is the reservation cost per reserved vertex.

cs.DS

Bounds for c-Ideal Hashing

In this paper, we analyze hashing from a worst-case perspective. To this end, we study a new property of hash families that is strongly related to d-perfect hashing, namely c-ideality. On the one hand, this notion generalizes the definition of perfect hashing, which has been studied extensively; on the other hand, it provides a direct link to the notion of c-approximativity. We focus on the usually neglected case where the average load αis at least 1 and prove upper and lower parametrized bounds on the minimal size of c-ideal hash families. As an aside, we show how c-ideality helps to analyze the advice complexity of hashing. The concept of advice, introduced a decade ago, lets us measure the information content of an online problem. We prove hashing's advice complexity to be linear in the hash table size.

cs.DS

Zero-Memory Graph Exploration with Unknown Inports

We study a very restrictive graph exploration problem. In our model, an agent without persistent memory is placed on a vertex of a graph and only sees the adjacent vertices. The goal is to visit every vertex of the graph, return to the start vertex, and terminate. The agent does not know through which edge it entered a vertex. The agent may color the current vertex and can see the colors of the neighboring vertices in an arbitrary order. The agent may not recolor a vertex. We investigate the number of colors necessary and sufficient to explore all graphs. We prove that n-1 colors are necessary and sufficient for exploration in general, 3 colors are necessary and sufficient if only trees are to be explored, and min(2k-3,n-1) colors are necessary and min(2k-1,n-1) colors are sufficient on graphs of size n and circumference $k$, where the circumference is the length of a longest cycle. This only holds if an algorithm has to explore all graphs and not merely certain graph classes. We give an example for a graph class where each graph can be explored with 4 colors, although the graphs have maximal circumference. Moreover, we prove that recoloring vertices is very powerful by designing an algorithm with recoloring that uses only 7 colors and explores all graphs.

cs.DS

Priority Algorithms with Advice for Disjoint Path Allocation Problems

We analyze the Disjoint Path Allocation problem (DPA) in the priority framework. Motivated by the problem of traffic regulation in communication networks, DPA consists of allocating edge-disjoint paths in a graph. While online algorithms for DPA have been thoroughly studied in the past, we extend the analysis of this optimization problem by considering the more powerful class of priority algorithms. Like an online algorithm, a priority algorithm receives its input only sequentially and must output irrevocable decisions for individual input items before having seen the input in its entirety. However, in contrast to the online setting, a priority algorithm may choose an order on the set of all possible input items and the actual input is then presented according to this order. A priority algorithm is a natural model for the intuitively well-understood concept of a greedy algorithm. Apart from analyzing the classical priority setting, we also consider priority algorithms with advice. Originally conceived to study online algorithms from an information-theoretic point of view, the concept of advice has recently been extended to the priority framework. In this paper, we analyze the classical variant of the DPA problem on the graph class of paths, the related problem of Length-Weighted DPA, and finally, DPA on the graph class of trees. We show asymptotically matching upper and lower bounds on the advice necessary for optimality in LWDPA and generalize the known optimality result for DPA on paths to trees with maximal degree at most 3. On trees with maximal degree greater than 3, we prove matching upper and lower bounds on the approximation ratio in the advice-free priority setting. Finally, we present upper and lower bounds on the advice necessary to achieve optimality on such trees.

cs.DS

Online Simple Knapsack with Reservation Costs

In the online simple knapsack problem items are presented in an iterative fashion and an algorithm has to decide for each item whether to reject or permanently include it into the knapsack without any knowledge about the rest of the instance. The goal is to pack the knapsack as full as possible. In this work, we introduce the option of reserving items for the cost of a fixed fraction $α$ of their size. An algorithm may pay this fraction in order to postpone its decision on whether to include or reject these items until after the last item of the instance was presented. While the classical online simple knapsack problem does not admit any constantly bounded competitive ratio in the deterministic setting, we find that adding the possibility of reservation makes the problem constantly competitive. We give tight bounds for the whole range of $α$ from $0$ to $1$.

cs.DS

From Finite-Valued Nondeterministic Transducers to Deterministic Two-Tape Automata

The question whether P equals NP revolves around the discrepancy between active production and mere verification by Turing machines. In this paper, we examine the analogous problem for finite transducers and automata. Every nondeterministic finite transducer defines a binary relation associating each input word with all output words that the transducer can successfully produce on the given input. Finite-valued transducers are those for which there is a finite upper bound on the number of output words that the relation associates with every input word. We characterize finite-valued, functional, and unambiguous nondeterministic transducers whose relations can be verified by a deterministic two-tape automaton, show how to construct such an automaton if one exists, and prove the undecidability of the criterion.

cs.CC

Complexity of Stability

Graph parameters such as the clique number, the chromatic number, and the independence number are central in many areas, ranging from computer networks to linguistics to computational neuroscience to social networks. In particular, the chromatic number of a graph (i.e., the smallest number of colors needed to color all vertices such that no two adjacent vertices are of the same color) can be applied in solving practical tasks as diverse as pattern matching, scheduling jobs to machines, allocating registers in compiler optimization, and even solving Sudoku puzzles. Typically, however, the underlying graphs are subject to (often minor) changes. To make these applications of graph parameters robust, it is important to know which graphs are stable for them in the sense that adding or deleting single edges or vertices does not change them. We initiate the study of stability of graphs for such parameters in terms of their computational complexity. We show that, for various central graph parameters, the problem of determining whether or not a given graph is stable is complete for Θ_2^p, a well-known complexity class in the second level of the polynomial hierarchy, which is also known as "parallel access to NP."

cs.CC

Efficient Circuit Simulation in MapReduce

The MapReduce framework has firmly established itself as one of the most widely used parallel computing platforms for processing big data on tera- and peta-byte scale. Approaching it from a theoretical standpoint has proved to be notoriously difficult, however. In continuation of Goodrich et al.'s early efforts, explicitly espousing the goal of putting the MapReduce framework on footing equal to that of long-established models such as the PRAM, we investigate the obvious complexity question of how the computational power of MapReduce algorithms compares to that of combinational Boolean circuits commonly used for parallel computations. Relying on the standard MapReduce model introduced by Karloff et al. a decade ago, we develop an intricate simulation technique to show that any problem in NC (i.e., a problem solved by a logspace-uniform family of Boolean circuits of polynomial size and a depth polylogarithmic in the input size) can be solved by a MapReduce computation in O(T(n)/ log n) rounds, where n is the input size and T(n) is the depth of the witnessing circuit family. Thus, we are able to closely relate the standard, uniform NC hierarchy modeling parallel computations to the deterministic MapReduce hierarchy DMRC by proving that NC^(i+1) is contained in DMRC^i for all natural i, including 0. Besides the theoretical significance, this result that has important applied aspects as well. In particular, we show for all problems in NC^1---many practically relevant ones such as integer multiplication and division, the parity function, and recognizing balanced strings of parentheses being among these---how to solve them in a constant number of deterministic MapReduce rounds.

cs.CC