arXiv subjects

Nicole Wein

Publications and source records attributed to Nicole Wein.

At least 19 recordsLinked to original sources

arXiv2026

Exponential Lower Bounds for Integer-Weighted Shortest-Paths Preservers of DAGs

We study a graph simplification problem introduced by Bernstein, Bodwin, and Wein [ITCS'24]. We start with a graph with arbitrarily large positive edge weights and the goal is to reweight the edges to small aspect ratio (ratio between largest and smallest weight) while preserving the shortest paths structure (the sequence of vertices and edges along shortest paths). They studied whether polynomial aspect ratio is always possible. They proved that for general graphs, both directed and undirected, it is not: there exist graphs for which any shortest-paths preserving reweighting requires exponential aspect ratio. In contrast, they showed that every DAG (directed acyclic graph) admits a reweighting with linear aspect ratio. However, the resulting edge weights are not integers. This motivated them to pose the open question of whether all DAGs admit a reweighting with polynomially-bounded integer edge weights. Our main result is to answer this question in the negative: we prove that there exist DAGs for which any shortest-paths preserving integer reweighting requires weights of size $2^{Ω(n)}$. In fact, this is even true when the DAG has very simple structure: 3 layers of vertices with only 3 vertices in the middle layer. In contrast, we show that if the number of vertices in the middle layer is decreased to 2, then a linear upper bound is possible. We extend our exponential lower bound to the approximate version of the problem where only a single $α$-approximate shortest path in the original graph must be preserved as an exact shortest path in the reweighted graph. Our exponential lower bound holds even for any finite approximation ratio $α>1$.

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arXiv2026

Optimal Non-Adaptive Vantage Point Selection

We study the \emph{vantage point selection} problem, introduced by Ashvinkumar, Chowdhury, Gao, Goswami, Mitchell, and Polishchuk [WADS'25] to model the problem of estimating bottleneck capacities on the Internet. The input is a weighted undirected graph with unique shortest paths where every edge has a distinct unknown \emph{capacity}. When the algorithm \emph{queries} a vertex $v$, it reveals the minimum-capacity edge on the shortest path from $v$ to every other vertex reachable from $v$. The goal is to maximize the total number of revealed edges. The quality of an algorithm is measured by its competitive ratio against an optimal algorithm that knows all edge capacities a priori. We first consider the foundational single-query setting, where both the algorithm and the optimal algorithm are restricted to a single query. There is a trivial upper bound of $O(n)$ on the competitive ratio and the best known lower bound was $\tildeΩ(\sqrt{n})$. We provide an algorithm and matching lower bound (up to polylogarithmic factors) showing that the best possible competitive ratio is $\tildeΘ(n^{2/3})$. Furthermore, we extend our results to the general setting where the optimal algorithm is allowed $k$ queries and our algorithm is allowed $αk$ queries for $α\geq 1$. We present a randomized non-adaptive algorithm and matching lower bound (up to polylogarithmic factors) showing that the best possible expected competitive ratio for non-adaptive algorithms is the following surprisingly complex bound: $$ \tildeΘ\left( \min\left\{ \frac{n}{αk}, \max\left( \sqrt{\frac{n}α}, \frac{n^{2/3}}{αk^{1/3}} \right) \right\} \right). $$

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arXiv2026

DAG Covers for Structured Graphs: The Steiner Point Effect

Given a weighted digraph $G$, a $(t,g,μ)$-DAG cover is a collection of $g$ dominating DAGs $D_1,\dots,D_g$ such that all distances are approximately preserved: for every pair $(u,v)$ of vertices, $\min_id_{D_i}(u,v)\le t\cdot d_{G}(u,v)$, and the total number of non-$G$ edges is bounded by $|(\cup_i D_i)\setminus G|\le μ$. Assadi, Hoppenworth, and Wein [STOC 25] and Filtser [SODA 26] studied DAG covers for general digraphs. This paper initiates the study of \emph{Steiner} DAG cover, where the DAGs are allowed to contain Steiner points. We obtain Steiner DAG covers on the important classes of planar digraphs and low-treewidth digraphs. Specifically, we show that any digraph with treewidth tw admits a $(1,2,\tilde{O}(n\cdot tw))$-Steiner DAG cover. For planar digraphs we provide a $(1+\varepsilon,2,\tilde{O}_\varepsilon(n))$-Steiner DAG cover. We also demonstrate a stark difference between Steiner and non-Steiner DAG covers. As a lower bound, we show that any non-Steiner DAG cover for graphs with treewidth $1$ with stretch $t<2$ and sub-quadratic number of extra edges requires $Ω(\log n)$ DAGs.

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arXiv2026

Tight Bounds for Memory Allocation With and Without Request Fragmentation

The classical memory-allocation problem captures the task of placing objects of different sizes in memory, while minimizing the so-called memory high-water mark. It has been known since the early 1970s that the optimal competitive ratio for any deterministic online allocator is $Θ(\log M)$, where $M$ is the volume high-water mark of the underlying request sequence. This paper begins with a simple observation: many real-world allocators seem to bypass the 1971 lower bound by adopting a slightly different model for memory allocation. These allocators use what we call $k$-aggregate request fragmentation, meaning that the memory allocator is permitted to break requests into multiple fragments, so long as the all-time maximum number of simultaneous fragments is at most $k$ times the all-time maximum number of simultaneous requests. We consider the following basic question: Does request fragmentation fundamentally change the problem of memory allocation, and if so, how? Our results come with several surprises. Among these, we find that even using $k = 1 + o(1)$ request fragmentation, the optimal competitive ratio---which was $Θ(\log M)$ in the classical setting---collapses to $Θ(\log \log M)$. This result is shown to be tight with matching upper and lower bounds, applying to both deterministic and randomized algorithms.

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arXiv2026

A Polynomial-Time Algorithm for the Next-to-Shortest Path Problem on Positively Weighted Directed Graphs

Given a graph and a pair of terminals $s$, $t$, the next-to-shortest path problem asks for an $s\!\to \!t$ (simple) path that is shortest among all not shortest $s\!\to \!t$ paths (if one exists). This problem was introduced in 1996, and soon after was shown to be NP-complete for directed graphs with non-negative edge weights, leaving open the case of positive edge weights. Subsequent work investigated this open question, and developed polynomial-time algorithms for the cases of undirected graphs and planar directed graphs. In this work, we resolve this nearly 30-year-old open problem by providing an algorithm for the next-to-shortest path problem on directed graphs with positive edge weights.

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arXiv2026

Bounding the Fragmentation of B-Trees Subject to Batched Insertions

The issue of internal fragmentation in data structures is a fundamental challenge in database design. A seminal result of Yao in this field shows that evenly splitting the leaves of a B-tree against a workload of uniformly random insertions achieves space utilization of around 69%. However, many database applications perform batched insertions, where a small run of consecutive keys is inserted at a single position. We develop a generalization of Yao's analysis to provide rigorous treatment of such batched workloads. Our approach revisits and reformulates the analytical structure underlying Yao's result in a way that enables generalization and is used to argue that even splitting works well for many workloads in our extended class. For the remaining workloads, we develop simple alternative strategies that provably maintain good space utilization.

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arXiv2025

Improved Online Sorting

We study the online sorting problem, where $n$ real numbers arrive in an online fashion, and the algorithm must immediately place each number into an array of size $(1+\varepsilon) n$ before seeing the next number. After all $n$ numbers are placed into the array, the cost is defined as the sum over the absolute differences of all $n-1$ pairs of adjacent numbers in the array, ignoring empty array cells. Aamand, Abrahamsen, Beretta, and Kleist introduced the problem and obtained a deterministic algorithm with cost $2^{O\left(\sqrt{\log n \cdot\log\log n +\log \varepsilon^{-1}}\right)}$, and a lower bound of $Ω(\log n / \log\log n)$ for deterministic algorithms. We obtain a deterministic algorithm with quasi-polylogarithmic cost $\left(\varepsilon^{-1}\log n\right)^{O\left(\log \log n\right)}$. Concurrent and independent work by Azar, Panigrahi, and Vardi achieves polylogarithmic cost $O(\varepsilon^{-1}\log^2 n)$.

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arXiv2025

Settling Weighted Token Swapping up to Algorithmic Barriers

We study the weighted token swapping problem, in which we are given a graph on $n$ vertices, $n$ weighted tokens, an initial assignment of one token to each vertex, and a final assignment of one token to each vertex. The goal is to find a minimum-cost sequence of swaps of adjacent tokens to reach the final assignment from the initial assignment, where the cost is the sum over all swaps of the sum of the weights of the two swapped tokens. Unweighted token swapping has been extensively studied: it is NP-hard to approximate to a factor better than $14/13$, and there is a polynomial-time 4-approximation, along with a tight "barrier" result showing that the class of locally optimal algorithms cannot achieve a ratio better than 4. For trees, the problem remains NP-hard to solve exactly, and there is a polynomial-time 2-approximation, along with a tight barrier result showing that the class of $\ell$-straying algorithms cannot achieve a ratio better than 2. Weighted token swapping with $\{0,1\}$ weights is much harder to approximation: it is NP-hard to approximate even to a factor of $(1-\varepsilon) \cdot \ln n$ for any constant $\varepsilon>0$. Restricting to positive weights, no approximation algorithms are known, and the only known lower bounds are those inherited directly from the unweighted version. We provide the first approximation algorithms for weighted token swapping on both trees and general graphs, along with tight barrier results. Letting $w$ and $W$ be the minimum and maximum token weights, our approximation ratio is $2+2W/w$ for general graphs and $1+W/w$ for trees.

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arXiv2025

Are there graphs whose shortest path structure requires large edge weights?

The aspect ratio of a (positively) weighted graph $G$ is the ratio of its maximum edge weight to its minimum edge weight. Aspect ratio commonly arises as a complexity measure in graph algorithms, especially related to the computation of shortest paths. Popular paradigms are to interpolate between the settings of weighted and unweighted input graphs by incurring a dependence on aspect ratio, or by simply restricting attention to input graphs of low aspect ratio. This paper studies the effects of these paradigms, investigating whether graphs of low aspect ratio have more structured shortest paths than graphs in general. In particular, we raise the question of whether one can generally take a graph of large aspect ratio and reweight its edges, to obtain a graph with bounded aspect ratio while preserving the structure of its shortest paths. Our findings are: - Every weighted DAG on $n$ nodes has a shortest-paths preserving graph of aspect ratio $O(n)$. A simple lower bound shows that this is tight. - The previous result does not extend to general directed or undirected graphs; in fact, the answer turns out to be exponential in these settings. In particular, we construct directed and undirected $n$-node graphs for which any shortest-paths preserving graph has aspect ratio $2^{Ω(n)}$. We also consider the approximate version of this problem, where the goal is for shortest paths in $H$ to correspond to approximate shortest paths in $G$. We show that our exponential lower bounds extend even to this setting. We also show that in a closely related model, where approximate shortest paths in $H$ must also correspond to approximate shortest paths in $G$, even DAGs require exponential aspect ratio.

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arXiv2025

Covering Approximate Shortest Paths with DAGs

We define and study analogs of probabilistic tree embedding and tree cover for directed graphs. We define the notion of a DAG cover of a general directed graph $G$: a small collection $D_1,\dots D_g$ of DAGs so that for all pairs of vertices $s,t$, some DAG $D_i$ provides low distortion for $dist(s,t)$; i.e. $ dist_G(s, t) \le \min_{i \in [g]} dist_{D_i}(s, t) \leq α\cdot dist_G(s, t)$, where $α$ is the distortion. As a trivial upper bound, there is a DAG cover with $n$ DAGs and $α=1$ by taking the shortest-paths tree from each vertex. When each DAG is restricted to be a subgraph of $G$, there is a matching lower bound (via a directed cycle) that $n$ DAGs are necessary, even to preserve reachability. Thus, we allow the DAGs to include a limited number of additional edges not in the original graph. When $n^2$ additional edges are allowed, there is a simple upper bound of two DAGs and $α=1$. Our first result is an almost-matching lower bound that even for $n^{2-o(1)}$ additional edges, at least $n^{1-o(1)}$ DAGs are needed, even to preserve reachability. However, the story is different when the number of additional edges is $\tilde{O}(m)$, a natural setting where the sparsity of the DAG collection nearly matches the original graph. Our main upper bound is that there is a near-linear time algorithm to construct a DAG cover with $\tilde{O}(m)$ additional edges, polylogarithmic distortion, and only $O(\log n)$ DAGs. This is similar to known results for undirected graphs: the well-known FRT probabilistic tree embedding implies a tree cover where both the number of trees and the distortion are logarithmic. Our algorithm also extends to a certain probabilistic embedding guarantee. Lastly, we complement our upper bound with a lower bound showing that achieving a DAG cover with no distortion and $\tilde{O}(m)$ additional edges requires a polynomial number of DAGs.

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arXiv2025

Beyond 2-approximation for k-Center in Graphs

We consider the classical $k$-Center problem in undirected graphs. The problem is known to have a polynomial-time 2-approximation. There are even $(2+\varepsilon)$-approximations running in near-linear time. The conventional wisdom is that the problem is closed, as $(2-\varepsilon)$-approximation is NP-hard when $k$ is part of the input, and for constant $k\geq 2$ it requires $n^{k-o(1)}$ time under SETH. Our first set of results show that one can beat the multiplicative factor of $2$ in undirected unweighted graphs if one is willing to allow additional small additive error, obtaining $(2-\varepsilon,O(1))$ approximations. We provide several algorithms that achieve such approximations for all integers $k$ with running time $O(n^{k-δ})$ for $δ>0$. For instance, for every $k\geq 2$, we obtain an $O(mn + n^{k/2+1})$ time $(2 - \frac{1}{2k-1}, 1 - \frac{1}{2k-1})$-approximation to $k$-Center. For $2$-Center we also obtain an $\tilde{O}(mn^{ω/3})$ time $(5/3,2/3)$-approximation algorithm. Notably, the running time of this $2$-Center algorithm is faster than the time needed to compute APSP. Our second set of results are strong fine-grained lower bounds for $k$-Center. We show that our $(3/2,O(1))$-approximation algorithm is optimal, under SETH, as any $(3/2-\varepsilon,O(1))$-approximation algorithm requires $n^{k-o(1)}$ time. We also give a time/approximation trade-off: under SETH, for any integer $t\geq 1$, $n^{k/t^2-1-o(1)}$ time is needed for any $(2-1/(2t-1),O(1))$-approximation algorithm for $k$-Center. This explains why our $(2-\varepsilon,O(1))$ approximation algorithms have $k$ appearing in the exponent of the running time. Our reductions also imply that, assuming ETH, the approximation ratio 2 of the known near-linear time algorithms cannot be improved by any algorithm whose running time is a polynomial independent of $k$, even if one allows additive error.

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arXiv2024

Edge-Minimum Walk of Modular Length in Polynomial Time

We study the problem of finding, in a directed graph, an st-walk of length r mod q which is edge-minimum, i.e., uses the smallest number of distinct edges. Despite the vast literature on paths and cycles with modularity constraints, to the best of our knowledge we are the first to study this problem. Our main result is a polynomial-time algorithm that solves this task when r and q are constants. We also show how our proof technique gives an algorithm to solve a generalization of the well-known Directed Steiner Network problem, in which connections between endpoint pairs are required to satisfy modularity constraints on their length. Our algorithm is polynomial when the number of endpoint pairs and the modularity constraints on the pairs are constants.

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arXiv2024

Improved Hardness-of-Approximation for Token Swapping

We study the token swapping problem, in which we are given a graph with an initial assignment of one distinct token to each vertex, and a final desired assignment (again with one token per vertex). The goal is to find the minimum length sequence of swaps of adjacent tokens required to get from the initial to final assignment. The token swapping problem is known to be NP-complete. It is also known to have a polynomial-time 4-approximation algorithm. From the hardness-of-approximation side, it is known to be NP-hard to approximate with ratio better than 1001/1000. Our main result is an improvement of the approximation ratio of the lower bound: We show that it is NP-hard to approximate with ratio better than 14/13. We then turn our attention to the 0/1-weighted version, in which every token has a weight of either 0 or 1, and the cost of a swap is the sum of the weights of the two participating tokens. Unlike standard token swapping, no constant-factor approximation is known for this version, and we provide an explanation. We prove that 0/1-weighted token swapping is NP-hard to approximate with ratio better than $(1-\varepsilon) \ln(n)$ for any constant $ε>0$. Lastly, we prove two barrier results for the standard (unweighted) token swapping problem. We show that one cannot beat the current best known approximation ratio of 4 using a large class of algorithms which includes all known algorithms, nor can one beat it using a common analysis framework.

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arXiv2024

Low Sensitivity Hopsets

Given a weighted graph $G$, a $(β,\varepsilon)$-hopset $H$ is an edge set such that for any $s,t \in V(G)$, where $s$ can reach $t$ in $G$, there is a path from $s$ to $t$ in $G \cup H$ which uses at most $β$ hops whose length is in the range $[dist_G(s,t), (1+\varepsilon)dist_G(s,t)]$. We break away from the traditional question that asks for a hopset that achieves small $|H|$ and instead study its sensitivity, a new quality measure which, informally, is the maximum number of times a vertex (or edge) is bypassed by an edge in $H$. The highlights of our results are: (i) $(\widetilde{O}(\sqrt{n}),0)$-hopsets on undirected graphs with $O(\log n)$ sensitivity, complemented with a lower bound showing that $\widetilde{O}(\sqrt{n})$ is tight up to polylogarithmic factors for any construction with polylogarithmic sensitivity. (ii) $(n^{o(1)},\varepsilon)$-hopsets on undirected graphs with $n^{o(1)}$ sensitivity for any $\varepsilon > 0$ that is at least inverse polylogarithmic, complemented with a lower bound on the tradeoff between $β, \varepsilon$, and the sensitivity. (iii) $\widetilde{O}(\sqrt{n})$-shortcut sets on directed graphs with $O(\log n)$ sensitivity, complemented with a lower bound showing that $β= \widetildeΩ(n^{1/3})$ for any construction with polylogarithmic sensitivity. We believe hopset sensitivity is a natural measure in and of itself, and could potentially find use in a diverse range of contexts. More concretely, the notion of hopset sensitivity is also directly motivated by the Differentially Private All Sets Range Queries problem. Our result for $O(\log n)$ sensitivity $(\widetilde{O}(\sqrt{n}),0)$-hopsets on undirected graphs immediately improves the current best-known upper bound on utility from $\widetilde{O}(n^{1/3})$ to $\widetilde{O}(n^{1/4})$ in the pure-DP setting, which is tight up to polylogarithmic factors.

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arXiv2024

Detecting Disjoint Shortest Paths in Linear Time and More

In the $k$-Disjoint Shortest Paths ($k$-DSP) problem, we are given a weighted graph $G$ on $n$ nodes and $m$ edges with specified source vertices $s_1, \dots, s_k$, and target vertices $t_1, \dots, t_k$, and are tasked with determining if $G$ contains vertex-disjoint $(s_i,t_i)$-shortest paths. For any constant $k$, it is known that $k$-DSP can be solved in polynomial time over undirected graphs and directed acyclic graphs (DAGs). However, the exact time complexity of $k$-DSP remains mysterious, with large gaps between the fastest known algorithms and best conditional lower bounds. In this paper, we obtain faster algorithms for important cases of $k$-DSP, and present better conditional lower bounds for $k$-DSP and its variants. Previous work solved 2-DSP over weighted undirected graphs in $O(n^7)$ time, and weighted DAGs in $O(mn)$ time. For the main result of this paper, we present linear time algorithms for solving 2-DSP on weighted undirected graphs and DAGs. Our algorithms are algebraic however, and so only solve the detection rather than search version of 2-DSP. For lower bounds, prior work implied that $k$-Clique can be reduced to $2k$-DSP in DAGs and undirected graphs with $O((kn)^2)$ nodes. We improve this reduction, by showing how to reduce from $k$-Clique to $k$-DSP in DAGs and undirected graphs with $O((kn)^2)$ nodes. A variant of $k$-DSP is the $k$-Disjoint Paths ($k$-DP) problem, where the solution paths no longer need to be shortest paths. Previous work reduced from $k$-Clique to $p$-DP in DAGs with $O(kn)$ nodes, for $p= k + k(k-1)/2$. We improve this by showing a reduction from $k$-Clique to $p$-DP, for $p=k + \lfloor k^2/4\rfloor$. Under the $k$-Clique Hypothesis from fine-grained complexity, our results establish better conditional lower bounds for $k$-DSP for all $k\ge 4$, and better conditional lower bounds for $p$-DP for all $p\le 4031$.

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arXiv2024

Additive Spanner Lower Bounds with Optimal Inner Graph Structure

We construct $n$-node graphs on which any $O(n)$-size spanner has additive error at least $+Ω(n^{3/17})$, improving on the previous best lower bound of $Ω(n^{1/7})$ [Bodwin-Hoppenworth FOCS '22]. Our construction completes the first two steps of a particular three-step research program, introduced in prior work and overviewed here, aimed at producing tight bounds for the problem by aligning aspects of the upper and lower bound constructions. More specifically, we develop techniques that enable the use of inner graphs in the lower bound framework whose technical properties are provably tight with the corresponding assumptions made in the upper bounds. As an additional application of our techniques, we improve the corresponding lower bound for $O(n)$-size additive emulators to $+Ω(n^{1/14})$.

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arXiv2024

Closing the Gap Between Directed Hopsets and Shortcut Sets

For an n-vertex directed graph $G = (V,E)$, a $β$-\emph{shortcut set} $H$ is a set of additional edges $H \subseteq V \times V$ such that $G \cup H$ has the same transitive closure as $G$, and for every pair $u,v \in V$, there is a $uv$-path in $G \cup H$ with at most $β$ edges. A natural generalization of shortcut sets to distances is a $(β,ε)$-\emph{hopset} $H \subseteq V \times V$, where the requirement is that $H$ and $G \cup H$ have the same shortest-path distances, and for every $u,v \in V$, there is a $(1+ε)$-approximate shortest path in $G \cup H$ with at most $β$ edges. There is a large literature on the tradeoff between the size of a shortcut set / hopset and the value of $β$. We highlight the most natural point on this tradeoff: what is the minimum value of $β$, such that for any graph $G$, there exists a $β$-shortcut set (or a $(β,ε)$-hopset) with $O(n)$ edges? Not only is this a natural structural question in its own right, but shortcuts sets / hopsets form the core of many distributed, parallel, and dynamic algorithms for reachability / shortest paths. Until very recently the best known upper bound was a folklore construction showing $β= O(n^{1/2})$, but in a breakthrough result Kogan and Parter [SODA 2022] improve this to $β= \tilde{O}(n^{1/3})$ for shortcut sets and $\tilde{O}(n^{2/5})$ for hopsets. Our result is to close the gap between shortcut sets and hopsets. That is, we show that for any graph $G$ and any fixed $ε$ there is a $(\tilde{O}(n^{1/3}),ε)$ hopset with $O(n)$ edges. More generally, we achieve a smooth tradeoff between hopset size and $β$ which exactly matches the tradeoff of Kogan and Parter for shortcut sets (up to polylog factors). Using a very recent black-box reduction of Kogan and Parter, our new hopset implies improved bounds for approximate distance preservers.

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arXiv2023

Better Lower Bounds for Shortcut Sets and Additive Spanners via an Improved Alternation Product

We obtain improved lower bounds for additive spanners, additive emulators, and diameter-reducing shortcut sets. Spanners and emulators are sparse graphs that approximately preserve the distances of a given graph. A shortcut set is a set of edges that when added to a directed graph, decreases its diameter. The previous best known lower bounds for these three structures are given by Huang and Pettie [SWAT 2018]. For $O(n)$-sized spanners, we improve the lower bound on the additive stretch from $Ω(n^{1/11})$ to $Ω(n^{2/21})$. For $O(n)$-sized emulators, we improve the lower bound on the additive stretch from $Ω(n^{1/18})$ to $Ω(n^{1/16})$. For $O(m)$-sized shortcut sets, we improve the lower bound on the graph diameter from $Ω(n^{1/11})$ to $Ω(n^{1/8})$. Our key technical contribution, which is the basis of all of our bounds, is an improvement of a graph product known as an alternation product.

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