Hu Fu, P. Lu, Zhihao Gavin Tang, Abner Turkieltaub, Hongxun Wu, Jinzhao Wu, Qianfan Zhang
{"title":"Oblivious Online Contention Resolution Schemes","authors":"Hu Fu, P. Lu, Zhihao Gavin Tang, Abner Turkieltaub, Hongxun Wu, Jinzhao Wu, Qianfan Zhang","doi":"10.1137/1.9781611977066.20","DOIUrl":"https://doi.org/10.1137/1.9781611977066.20","url":null,"abstract":"Contention resolution schemes (CRSs) are powerful tools for obtaining\"ex post feasible\"solutions from candidates that are drawn from\"ex ante feasible\"distributions. Online contention resolution schemes (OCRSs), the online version, have found myriad applications in Bayesian and stochastic problems, such as prophet inequalities and stochastic probing. When the ex ante distribution is unknown, it was unknown whether good CRSs/OCRSs exist with no sample (in which case the scheme is oblivious) or few samples from the distribution. In this work, we give a simple $frac{1}{e}$-selectable oblivious single item OCRS by mixing two simple schemes evenly, and show, via a Ramsey theory argument, that it is optimal. On the negative side, we show that no CRS or OCRS with $O(1)$ samples can be $Omega(1)$-balanced/selectable (i.e., preserve every active candidate with a constant probability) for graphic or transversal matroids.","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"82 6 1","pages":"268-278"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89587349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Simple Approximation Algorithm for Vector Scheduling and Applications to Stochastic Min-Norm Load Balancing","authors":"Sharat Ibrahimpur, Chaitanya Swamy","doi":"10.1137/1.9781611977066.18","DOIUrl":"https://doi.org/10.1137/1.9781611977066.18","url":null,"abstract":"We consider the Vector Scheduling problem on identical machines: we have m machines, and a set J of n jobs, where each job j has a processing-time vector pj ∈ R d ≥0. The goal is to find an assignment σ : J → [m] of jobs to machines so as to minimize the makespanmaxi∈[m] maxr∈[d] ( ∑","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"17 1","pages":"247-256"},"PeriodicalIF":0.0,"publicationDate":"2021-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86687709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Debarati Das, Evangelos Kipouridis, M. Gutenberg, Christian Wulff-Nilsen
{"title":"A Simple Algorithm for Multiple-Source Shortest Paths in Planar Digraphs","authors":"Debarati Das, Evangelos Kipouridis, M. Gutenberg, Christian Wulff-Nilsen","doi":"10.1137/1.9781611977066.1","DOIUrl":"https://doi.org/10.1137/1.9781611977066.1","url":null,"abstract":"Given an $n$-vertex planar embedded digraph $G$ with non-negative edge weights and a face $f$ of $G$, Klein presented a data structure with $O(nlog n)$ space and preprocessing time which can answer any query $(u,v)$ for the shortest path distance in $G$ from $u$ to $v$ or from $v$ to $u$ in $O(log n)$ time, provided $u$ is on $f$. This data structure is a key tool in a number of state-of-the-art algorithms and data structures for planar graphs. Klein's data structure relies on dynamic trees and the persistence technique as well as a highly non-trivial interaction between primal shortest path trees and their duals. The construction of our data structure follows a completely different and in our opinion very simple divide-and-conquer approach that solely relies on Single-Source Shortest Path computations and contractions in the primal graph. Our space and preprocessing time bound is $O(nlog |f|)$ and query time is $O(log |f|)$ which is an improvement over Klein's data structure when $f$ has small size.","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"66 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79469176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tight Bounds for Differentially Private Anonymized Histograms","authors":"Pasin Manurangsi","doi":"10.1137/1.9781611977066.14","DOIUrl":"https://doi.org/10.1137/1.9781611977066.14","url":null,"abstract":"In this note, we consider the problem of differentially privately (DP) computing an anonymized histogram, which is defined as the multiset of counts of the input dataset (without bucket labels). In the low-privacy regime $epsilon geq 1$, we give an $epsilon$-DP algorithm with an expected $ell_1$-error bound of $O(sqrt{n} / e^epsilon)$. In the high-privacy regime $epsilon<1$, we give an $Omega(sqrt{n log(1/epsilon) / epsilon})$ lower bound on the expected $ell_1$ error. In both cases, our bounds asymptotically match the previously known lower/upper bounds due to [Suresh, NeurIPS 2019].","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"17 1","pages":"203-213"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72836525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deterministic Approximation of Random Walks via Queries in Graphs of Unbounded Size","authors":"Edward Pyne, S. Vadhan","doi":"10.1137/1.9781611977066.5","DOIUrl":"https://doi.org/10.1137/1.9781611977066.5","url":null,"abstract":"Consider the following computational problem: given a regular digraph G = (V,E), two vertices u, v ∈ V , and a walk length t ∈ N, estimate the probability that a random walk of length t from u ends at v to within ±ε. A randomized algorithm can solve this problem by carrying out O(1/ε) random walks of length t from u and outputting the fraction that end at v. In this paper, we study deterministic algorithms for this problem that are also restricted to carrying out walks of length t from u and seeing which ones end at v. Specifically, if G is d-regular, the algorithm is given oracle access to a function f : [d] → {0, 1} where f(x) is 1 if the walk from u specified by the edge labels in x ends at v. We assume that G is consistently labelled, meaning that the edges of label i for each i ∈ [d] form a permutation on V . We show that there exists a deterministic algorithm that makes poly(dt/ε) nonadaptive queries to f , regardless of the number of vertices in the graph G. Crucially, and in contrast to the randomized algorithm, our algorithm does not simply output the average value of its queries. Indeed, Hoza, Pyne, and Vadhan (ITCS 2021) showed that any deterministic algorithm of the latter form that works for graphs of unbounded size must have query complexity at least exp(Ω̃(log(t) log(1/ε))). In the language of pseudorandomness, our result is a separation between the query complexity of “deterministic samplers” and “deterministic averaging samplers” for the class of “permutation branching programs of unbounded width”. Our separation is stronger than the prior separation of Pyne and Vadhan (CCC 2021), and has a much simpler proof (not using spectral graph theory or the Impagliazzo–Nisan–Wigderson pseudorandom generator). On the other hand, the algorithm of Pyne and Vadhan is explicit and computable in small space, whereas ours is not explicit (unless we assume the existence of an optimal explicit pseudorandom generator for permutation branching programs of bounded width).","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"28 1","pages":"57-67"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74897106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Parameterized Convexity Testing","authors":"A. Lahiri, I. Newman, Nithin M. Varma","doi":"10.1137/1.9781611977066.12","DOIUrl":"https://doi.org/10.1137/1.9781611977066.12","url":null,"abstract":"In this work, we develop new insights into the fundamental problem of convexity testing of real-valued functions over the domain [n]. Specifically, we present a nonadaptive algorithm that, given inputs ε ∈ (0, 1), s ∈ N, and oracle access to a function, ε-tests convexity in O(log(s)/ε), where s is an upper bound on the number of distinct discrete derivatives of the function. We also show that this bound is tight. Since s ≤ n, our query complexity bound is at least as good as that of the optimal convexity tester (Ben Eliezer; ITCS 2019) with complexity O( log εn ε ); our bound is strictly better when s = o(n). The main contribution of our work is to appropriately parameterize the complexity of convexity testing to circumvent the worst-case lower bound (Belovs et al.; SODA 2020) of Ω( log(εn) ε ) expressed in terms of the input size and obtain a more efficient algorithm.","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"1 1","pages":"174-181"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85306710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antoine M'eot, A. D. Mesmay, Moritz Mühlenthaler, Alantha Newman
{"title":"Voting algorithms for unique games on complete graphs","authors":"Antoine M'eot, A. D. Mesmay, Moritz Mühlenthaler, Alantha Newman","doi":"10.1137/1.9781611977585.ch12","DOIUrl":"https://doi.org/10.1137/1.9781611977585.ch12","url":null,"abstract":"An approximation algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying a $(1 - f(epsilon))$-fraction of the constraints on any $(1-epsilon)$-satisfiable instance, where the loss function $f$ is such that $f(epsilon) rightarrow 0$ as $epsilon rightarrow 0$. Moreover, the runtime of a robust algorithm should not depend in any way on $epsilon$. In this paper, we present such an algorithm for Min-Unique-Games on complete graphs with $q$ labels. Specifically, the loss function is $f(epsilon) = (epsilon + c_{epsilon} epsilon^2)$, where $c_{epsilon}$ is a constant depending on $epsilon$ such that $lim_{epsilon rightarrow 0} c_{epsilon} = 16$. The runtime of our algorithm is $O(qn^3)$ (with no dependence on $epsilon$) and can run in time $O(qn^2)$ using a randomized implementation with a slightly larger constant $c_{epsilon}$. Our algorithm is combinatorial and uses voting to find an assignment. It can furthermore be used to provide a PTAS for Min-Unique-Games on complete graphs, recovering a result of Karpinski and Schudy with a simpler algorithm and proof. We also prove NP-hardness for Min-Unique-Games on complete graphs and (using a randomized reduction) even in the case where the constraints form a cyclic permutation, which is also known as Min-Linear-Equations-mod-$q$ on complete graphs.","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"44 1","pages":"124-136"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85535974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finding Relevant Points for Nearest-Neighbor Classification","authors":"D. Eppstein","doi":"10.1137/1.9781611977066.6","DOIUrl":"https://doi.org/10.1137/1.9781611977066.6","url":null,"abstract":"In nearest-neighbor classification problems, a set of $d$-dimensional training points are given, each with a known classification, and are used to infer unknown classifications of other points by using the same classification as the nearest training point. A training point is relevant if its omission from the training set would change the outcome of some of these inferences. We provide a simple algorithm for thinning a training set down to its subset of relevant points, using as subroutines algorithms for finding the minimum spanning tree of a set of points and for finding the extreme points (convex hull vertices) of a set of points. The time bounds for our algorithm, in any constant dimension $dge 3$, improve on a previous algorithm for the same problem by Clarkson (FOCS 1994).","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"62 1","pages":"68-78"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84265598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards Non-Uniform k-Center with Constant Types of Radii","authors":"Xinrui Jia, Lars Rohwedder, K. Sheth, O. Svensson","doi":"10.1137/1.9781611977066.16","DOIUrl":"https://doi.org/10.1137/1.9781611977066.16","url":null,"abstract":"In the Non-Uniform k-Center problem we need to cover a finite metric space using k balls of different radii that can be scaled uniformly. The goal is to minimize the scaling factor. If the number of different radii is unbounded, the problem does not admit a constant-factor approximation algorithm but it has been conjectured that such an algorithm exists if the number of radii is constant. Yet, this is known only for the case of two radii. Our first contribution is a simple black box reduction which shows that if one can handle the variant of t− 1 radii with outliers, then one can also handle t radii. Together with an algorithm by Chakrabarty and Negahbani for two radii with outliers, this immediately implies a constant-factor approximation algorithm for three radii; thus making further progress on the conjecture. Furthermore, using algorithms for the k-center with outliers problem, that is the one radii with outliers case, we also get a simple algorithm for two radii. The algorithm by Chakrabarty and Negahbani uses a top-down approach, starting with the larger radius and then proceeding to the smaller one. Our reduction, on the other hand, looks only at the smallest radius and eliminates it, which suggests that a bottom-up approach is promising. In this spirit, we devise a modification of the Chakrabarty and Negahbani algorithm which runs in a bottom-up fashion, and in this way we recover their result with the advantage of having a simpler analysis. ∗Supported by the Swiss National Science Foundation project 200021-184656 “Randomness in Problem Instances and Randomized Algorithms.”","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"8 1","pages":"228-237"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76326593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Frequency-Domain Representation of First-Order Methods: A Simple and Robust Framework of Analysis","authors":"Ioannis Anagnostides, Ioannis Panageas","doi":"10.1137/1.9781611977066.10","DOIUrl":"https://doi.org/10.1137/1.9781611977066.10","url":null,"abstract":"Motivated by recent applications in min-max optimization, we employ tools from nonlinear control theory in order to analyze a class of\"historical\"gradient-based methods, for which the next step lies in the span of the previously observed gradients within a time horizon. Specifically, we leverage techniques developed by Hu and Lessard (2017) to build a frequency-domain framework which reduces the analysis of such methods to numerically-solvable algebraic tasks, establishing linear convergence under a class of strongly monotone and co-coercive operators. On the applications' side, we focus on the Optimistic Gradient Descent (OGD) method, which augments the standard Gradient Descent with an additional past-gradient in the optimization step. The proposed framework leads to a simple and sharp analysis of OGD -- and generalizations thereof -- under a much broader regime of parameters. Notably, this characterization directly extends under adversarial noise in the observed value of the gradient. Moreover, our frequency-domain framework provides an exact quantitative comparison between simultaneous and alternating updates of OGD. An interesting byproduct is that OGD -- and variants thereof -- is an instance of PID control, arguably one of the most influential algorithms of the last century; this observation sheds more light to the stabilizing properties of\"optimism\".","PeriodicalId":93491,"journal":{"name":"Proceedings of the SIAM Symposium on Simplicity in Algorithms (SOSA)","volume":"38 1","pages":"131-160"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73528270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}