We introduce a new type of adversary for online graph problems. The new adversary is parameterized by a single integer $κ$, which upper bounds the number of connected components that the adversary can use at any time during the presentation of the online graph $G$. We call this adversary "$κ$ components bounded", or $κ$-CB for short. On one hand, this adversary is restricted compared to the classical adversary because of the $κ$-CB constraint. On the other hand, we seek competitive ratios parameterized only by $κ$ with no dependence on the input length $n$, thereby giving the new adversary power to use arbitrarily large inputs.
We study online coloring under the $κ$-CB adversary. We obtain finer analysis of the existing algorithms $FirstFit$ and $CBIP$ by computing their competitive ratios on trees and bipartite graphs under the new adversary. Surprisingly, $FirstFit$ outperforms $CBIP$ on trees. When it comes to bipartite graphs $FirstFit$ is no longer competitive under the new adversary, while $CBIP$ uses at most $2κ$ colors. We also study several well known classes of graphs, such as $3$-colorable, $C_k$-free, $d$-inductive, planar, and bounded treewidth, with respect to online coloring under the $κ$-CB adversary. We demonstrate that the extra adversarial power of unbounded input length outweighs the restriction on the number of connected components leading to non existence of competitive algorithms for these classes.
我们考虑在线打集的问题。Hypergraph（即，由点和范围组成的范围空间）是预先已知的，并且以在线方式逐个地输入要插入的范围。在线算法必须在到达时插入每个范围。在线算法可以将点添加到命中集，但不能删除已经选择的点。目标是使用最少的点数。Alon等人的\cite{alon2009online}给出了一般超图的最著名的在线命中集竞争比是$O(\log n \cdot \log m)$，其中$n$和$m$分别表示点的个数和范围的个数.我们考虑超图，其中两个相交值域的并集也是一个值域。我们对这类超图的主要结果如下。在线碰集问题的竞争比至多是唯一极大数且至少是此数减1。
We give an online algorithm that with high probability computes a $\left(\frac{e}{e-1} + o(1)\right)Δ$ edge coloring on a graph $G$ with maximum degree $Δ= ω(\log n)$ under online edge arrivals against oblivious adversaries, making first progress on the conjecture of Bar-Noy, Motwani, and Naor in this general setting. Our algorithm is based on reducing to a matching problem on locally treelike graphs, and then applying a tree recurrences based approach for arguing correlation decay.
Nearly thirty years ago, Bar-Noy, Motwani and Naor [IPL'92] conjectured that an online $(1+o(1))Δ$-edge-coloring algorithm exists for $n$-node graphs of maximum degree $Δ=ω(\log n)$. This conjecture remains open in general, though it was recently proven for bipartite graphs under \emph{one-sided vertex arrivals} by Cohen et al.~[FOCS'19]. In a similar vein, we study edge coloring under widely-studied relaxations of the online model. Our main result is in the \emph{random-order} online model. For this model, known results fall short of the Bar-Noy et al.~conjecture, either in the degree bound […
Nearly three decades ago, Bar-Noy, Motwani and Naor showed that no online edge-coloring algorithm can edge color a graph optimally. Indeed, their work, titled "the greedy algorithm is optimal for on-line edge coloring", shows that the competitive ratio of $2$ of the naïve greedy algorithm is best possible online. However, their lower bound required bounded-degree graphs, of maximum degree $Δ= O(\log n)$, which prompted them to conjecture that better bounds are possible for higher-degree graphs. While progress has been made towards resolving this conjecture for restricted inputs and arrivals or…
Columbia University（哥伦比亚大学）；UC Berkeley（加州大学伯克利分校）；Google（谷歌）
We provide the first sub-linear space and sub-linear regret algorithm for online learning with expert advice (against an oblivious adversary), addressing an open question raised recently by Srinivas, Woodruff, Xu and Zhou (STOC 2022). We also demonstrate a separation between oblivious and (strong) adaptive adversaries by proving a linear memory lower bound of any sub-linear regret algorithm against an adaptive adversary. Our algorithm is based on a novel pool selection procedure that bypasses the traditional wisdom of leader selection for online learning, and a generic reduction that transform…
A streaming algorithm is considered to be adversarially robust if it provides correct outputs with high probability even when the stream updates are chosen by an adversary who may observe and react to the past outputs of the algorithm. We grow the burgeoning body of work on such algorithms in a new direction by studying robust algorithms for the problem of maintaining a valid vertex coloring of an $n$-vertex graph given as a stream of edges. Following standard practice, we focus on graphs with maximum degree at most $Δ$ and aim for colorings using a small number $f(Δ)$ of colors. A recent brea…
Real-world problems such as ad allocation and matching have been extensively studied under the lens of combinatorial optimization. In several applications, uncertainty in the input appears naturally and this has led to the study of online stochastic optimization models for such problems. For the offline case, these constrained combinatorial optimization problems have been extensively studied, and Contention Resolution Schemes (CRSs), introduced by Chekuri, Vondrák, and Zenklusen, have emerged in recent years as a general framework to obtaining a solution. The idea behind a CRS is to first obta…
In the bounded delay buffer management problem unit size packets arrive online to be sent over a network link. The objective is to maximize the total weight of packets sent before their deadline. In this paper we are interested in the two-valued variant of the problem, where every packet has either low (1) or high priority weight ($α$ > 1). We show that its randomized competitive ratio against an oblivious adversary is 1 + ($α$ -- 1)/($α$ 2 + $α$).
We study weighted edge coloring of graphs, where we are given an undirected edge-weighted general multi-graph $G := (V, E)$ with weights $w : E \rightarrow [0, 1]$. The goal is to find a proper weighted coloring of the edges with as few colors as possible. An edge coloring is called a proper weighted coloring if the sum of the weights of the edges incident to a vertex of any color is at most one. In the online setting, the edges are revealed one by one and have to be colored irrevocably as soon as they are revealed. We show that $3.39m+o(m)$ colors are enough when the maximum number of neighbo…
University of Pennsylvania（宾夕法尼亚大学）；Rice University（莱斯大学）
We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and so the minimax theorem does not apply), we give a simple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains, ranging from multicalibration to a large set of no …
We consider the selective graph coloring problem, which is a generalization of the classical graph coloring problem. Given a graph together with a partition of its vertex set into clusters, we want to choose exactly one vertex per cluster so that the number of colors needed to color the selected set of vertices is minimized. This problem is known to be NP-hard. In this study, we focus on an exact cutting plane algorithm for selective graph coloring in perfect graphs. Since there exists no suite of perfect graph instances to the best of our knowledge, we also propose an algorithm to randomly (b…
We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time. We give an algorithm that maintains $O(\sqrt{\log(nd/δ)})$ discrepancy with probability $1-δ$, matching the lower bound given in [Bansal et al. 2020] up to an $O(\sqrt{\log \log n})$ factor in the high-probability regime. We also provide results for the weighted and multi-color versions of the problem.
