TY - CONF AB - Oblivious routing is a well-studied paradigm that uses static precomputed routing tables for selecting routing paths within a network. Existing oblivious routing schemes with polylogarithmic competitive ratio for general networks are tree-based, in the sense that routing is performed according to a convex combination of trees. However, this restriction to trees leads to a construction that has time quadratic in the size of the network and does not parallelize well. In this paper we study oblivious routing schemes based on electrical routing. In particular, we show that general networks with n vertices and m edges admit a routing scheme that has competitive ratio O(log² n) and consists of a convex combination of only O(√m) electrical routings. This immediately leads to an improved construction algorithm with time Õ(m^{3/2}) that can also be implemented in parallel with Õ(√m) depth. AU - Goranci, Gramoz AU - Henzinger, Monika H AU - Räcke, Harald AU - Sachdeva, Sushant AU - Sricharan, A. R. ID - 15008 SN - 1868-8969 T2 - 15th Innovations in Theoretical Computer Science Conference TI - Electrical flows for polylogarithmic competitive oblivious routing VL - 287 ER - TY - CONF AB - For a set of points in Rd, the Euclidean k-means problems consists of finding k centers such that the sum of distances squared from each data point to its closest center is minimized. Coresets are one the main tools developed recently to solve this problem in a big data context. They allow to compress the initial dataset while preserving its structure: running any algorithm on the coreset provides a guarantee almost equivalent to running it on the full data. In this work, we study coresets in a fully-dynamic setting: points are added and deleted with the goal to efficiently maintain a coreset with which a k-means solution can be computed. Based on an algorithm from Henzinger and Kale [ESA'20], we present an efficient and practical implementation of a fully dynamic coreset algorithm, that improves the running time by up to a factor of 20 compared to our non-optimized implementation of the algorithm by Henzinger and Kale, without sacrificing more than 7% on the quality of the k-means solution. AU - Henzinger, Monika H AU - Saulpic, David AU - Sidl, Leonhard ID - 14769 T2 - 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments TI - Experimental evaluation of fully dynamic k-means via coresets ER - TY - JOUR AB - We present an auction algorithm using multiplicative instead of constant weight updates to compute a (1-E)-approximate maximum weight matching (MWM) in a bipartite graph with n vertices and m edges in time 0(mE-1), beating the running time of the fastest known approximation algorithm of Duan and Pettie [JACM ’14] that runs in 0(mE-1 log E-1). Our algorithm is very simple and it can be extended to give a dynamic data structure that maintains a (1-E)-approximate maximum weight matching under (1) one-sided vertex deletions (with incident edges) and (2) one-sided vertex insertions (with incident edges sorted by weight) to the other side. The total time time used is 0(mE-1), where m is the sum of the number of initially existing and inserted edges. AU - Zheng, Da Wei AU - Henzinger, Monika H ID - 15121 JF - Mathematical Programming SN - 0025-5610 TI - Multiplicative auction algorithm for approximate maximum weight bipartite matching ER - TY - CONF AB - We present a dynamic data structure for maintaining the persistent homology of a time series of real numbers. The data structure supports local operations, including the insertion and deletion of an item and the cutting and concatenating of lists, each in time O(log n + k), in which n counts the critical items and k the changes in the augmented persistence diagram. To achieve this, we design a tailor-made tree structure with an unconventional representation, referred to as banana tree, which may be useful in its own right. AU - Cultrera di Montesano, Sebastiano AU - Edelsbrunner, Herbert AU - Henzinger, Monika H AU - Ost, Lara ED - Woodruff, David P. ID - 15093 T2 - Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) TI - Dynamically maintaining the persistent homology of time series ER - TY - CONF AB - Dynamic programming (DP) is one of the fundamental paradigms in algorithm design. However, many DP algorithms have to fill in large DP tables, represented by two-dimensional arrays, which causes at least quadratic running times and space usages. This has led to the development of improved algorithms for special cases when the DPs satisfy additional properties like, e.g., the Monge property or total monotonicity. In this paper, we consider a new condition which assumes (among some other technical assumptions) that the rows of the DP table are monotone. Under this assumption, we introduce a novel data structure for computing (1 + ϵ)-approximate DP solutions in near-linear time and space in the static setting, and with polylogarithmic update times when the DP entries change dynamically. To the best of our knowledge, our new condition is incomparable to previous conditions and is the first which allows to derive dynamic algorithms based on existing DPs. Instead of using two-dimensional arrays to store the DP tables, we store the rows of the DP tables using monotone piecewise constant functions. This allows us to store length-n DP table rows with entries in [0, W] using only polylog(n, W) bits, and to perform operations, such as (min, +)-convolution or rounding, on these functions in polylogarithmic time. We further present several applications of our data structure. For bicriteria versions of k-balanced graph partitioning and simultaneous source location, we obtain the first dynamic algorithms with subpolynomial update times, as well as the first static algorithms using only near-linear time and space. Additionally, we obtain the currently fastest algorithm for fully dynamic knapsack. AU - Henzinger, Monika H AU - Neumann, Stefan AU - Räcke, Harald AU - Schmid, Stefan ID - 12760 SN - 1868-8969 T2 - 40th International Symposium on Theoretical Aspects of Computer Science TI - Dynamic maintenance of monotone dynamic programs and applications VL - 254 ER - TY - CONF AB - We show an (1+ϵ)-approximation algorithm for maintaining maximum s-t flow under m edge insertions in m1/2+o(1)ϵ−1/2 amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee. AU - Goranci, Gramoz AU - Henzinger, Monika H ID - 14085 SN - 1868-8969 T2 - 50th International Colloquium on Automata, Languages, and Programming TI - Efficient data structures for incremental exact and approximate maximum flow VL - 261 ER - TY - CONF AB - The maximization of submodular functions have found widespread application in areas such as machine learning, combinatorial optimization, and economics, where practitioners often wish to enforce various constraints; the matroid constraint has been investigated extensively due to its algorithmic properties and expressive power. Though tight approximation algorithms for general matroid constraints exist in theory, the running times of such algorithms typically scale quadratically, and are not practical for truly large scale settings. Recent progress has focused on fast algorithms for important classes of matroids given in explicit form. Currently, nearly-linear time algorithms only exist for graphic and partition matroids [Alina Ene and Huy L. Nguyen, 2019]. In this work, we develop algorithms for monotone submodular maximization constrained by graphic, transversal matroids, or laminar matroids in time near-linear in the size of their representation. Our algorithms achieve an optimal approximation of 1-1/e-ε and both generalize and accelerate the results of Ene and Nguyen [Alina Ene and Huy L. Nguyen, 2019]. In fact, the running time of our algorithm cannot be improved within the fast continuous greedy framework of Badanidiyuru and Vondrák [Ashwinkumar Badanidiyuru and Jan Vondrák, 2014]. To achieve near-linear running time, we make use of dynamic data structures that maintain bases with approximate maximum cardinality and weight under certain element updates. These data structures need to support a weight decrease operation and a novel Freeze operation that allows the algorithm to freeze elements (i.e. force to be contained) in its basis regardless of future data structure operations. For the laminar matroid, we present a new dynamic data structure using the top tree interface of Alstrup, Holm, de Lichtenberg, and Thorup [Stephen Alstrup et al., 2005] that maintains the maximum weight basis under insertions and deletions of elements in O(log n) time. This data structure needs to support certain subtree query and path update operations that are performed every insertion and deletion that are non-trivial to handle in conjunction. For the transversal matroid the Freeze operation corresponds to requiring the data structure to keep a certain set S of vertices matched, a property that we call S-stability. While there is a large body of work on dynamic matching algorithms, none are S-stable and maintain an approximate maximum weight matching under vertex updates. We give the first such algorithm for bipartite graphs with total running time linear (up to log factors) in the number of edges. AU - Henzinger, Monika H AU - Liu, Paul AU - Vondrák, Jan AU - Zheng, Da Wei ID - 14086 SN - 18688969 T2 - 50th International Colloquium on Automata, Languages, and Programming TI - Faster submodular maximization for several classes of matroids VL - 261 ER - TY - CONF AB - We study fine-grained error bounds for differentially private algorithms for counting under continual observation. Our main insight is that the matrix mechanism when using lower-triangular matrices can be used in the continual observation model. More specifically, we give an explicit factorization for the counting matrix Mcount and upper bound the error explicitly. We also give a fine-grained analysis, specifying the exact constant in the upper bound. Our analysis is based on upper and lower bounds of the completely bounded norm (cb-norm) of Mcount . Along the way, we improve the best-known bound of 28 years by Mathias (SIAM Journal on Matrix Analysis and Applications, 1993) on the cb-norm of Mcount for a large range of the dimension of Mcount. Furthermore, we are the first to give concrete error bounds for various problems under continual observation such as binary counting, maintaining a histogram, releasing an approximately cut-preserving synthetic graph, many graph-based statistics, and substring and episode counting. Finally, we note that our result can be used to get a fine-grained error bound for non-interactive local learning and the first lower bounds on the additive error for (ϵ,δ)-differentially-private counting under continual observation. Subsequent to this work, Henzinger et al. (SODA, 2023) showed that our factorization also achieves fine-grained mean-squared error. AU - Fichtenberger, Hendrik AU - Henzinger, Monika H AU - Upadhyay, Jalaj ID - 14462 T2 - Proceedings of the 40th International Conference on Machine Learning TI - Constant matters: Fine-grained error bound on differentially private continual observation VL - 202 ER - TY - JOUR AB - n the dynamic minimum set cover problem, the challenge is to minimize the update time while guaranteeing a close-to-optimal min{O(log n), f} approximation factor. (Throughout, n, m, f , and C are parameters denoting the maximum number of elements, the number of sets, the frequency, and the cost range.) In the high-frequency range, when f = Ω(log n) , this was achieved by a deterministic O(log n) -approximation algorithm with O(f log n) amortized update time by Gupta et al. [Online and dynamic algorithms for set cover, in Proceedings STOC 2017, ACM, pp. 537–550]. In this paper we consider the low-frequency range, when f = O(log n) , and obtain deterministic algorithms with a (1 + ∈)f -approximation ratio and the following guarantees on the update time. (1) O ((f/∈)-log(Cn)) amortized update time: Prior to our work, the best approximation ratio guaranteed by deterministic algorithms was O(f2) of Bhattacharya, Henzinger, and Italiano [Design of dynamic algorithms via primal-dual method, in Proceedings ICALP 2015, Springer, pp. 206–218]. In contrast, the only result with O(f) -approximation was that of Abboud et al. [Dynamic set cover: Improved algorithms and lower bounds, in Proceedings STOC 2019, ACM, pp. 114–125], who designed a randomized (1+∈)f -approximation algorithm with amortized update time. (2) O(f2/∈3 + (f/∈2).logC) amortized update time: This result improves the above update time bound for most values of f in the low-frequency range, i.e., f=o(log n) . It is also the first result that is independent of m and n. It subsumes the constant amortized update time of Bhattacharya and Kulkarni [Deterministically maintaining a (2 + ∈) -approximate minimum vertex cover in O(1/∈2) amortized update time, in Proceedings SODA 2019, SIAM, pp. 1872–1885] for unweighted dynamic vertex cover (i.e., when f = 2 and C = 1). (3) O((f/∈3).log2(Cn)) worst-case update time: No nontrivial worst-case update time was previously known for the dynamic set cover problem. Our bound subsumes and improves by a logarithmic factor the O(log3n/poly (∈)) worst-case update time for the unweighted dynamic vertex cover problem (i.e., when f = 2 and C =1) of Bhattacharya, Henzinger, and Nanongkai [Fully dynamic approximate maximum matching and minimum vertex cover in O(log3)n worst case update time, in Proceedings SODA 2017, SIAM, pp. 470–489]. We achieve our results via the primal-dual approach, by maintaining a fractional packing solution as a dual certificate. Prior work in dynamic algorithms that employs the primal-dual approach uses a local update scheme that maintains relaxed complementary slackness conditions for every set. For our first result we use instead a global update scheme that does not always maintain complementary slackness conditions. For our second result we combine the global and the local update schema. To achieve our third result we use a hierarchy of background schedulers. It is an interesting open question whether this background scheduler technique can also be used to transform algorithms with amortized running time bounds into algorithms with worst-case running time bounds. AU - Bhattacharya, Sayan AU - Henzinger, Monika H AU - Nanongkai, Danupon AU - Wu, Xiaowei ID - 14558 IS - 5 JF - SIAM Journal on Computing SN - 0097-5397 TI - Deterministic near-optimal approximation algorithms for dynamic set cover VL - 52 ER - TY - CONF AB - In all state-of-the-art sketching and coreset techniques for clustering, as well as in the best known fixed-parameter tractable approximation algorithms, randomness plays a key role. For the classic k-median and k-means problems, there are no known deterministic dimensionality reduction procedure or coreset construction that avoid an exponential dependency on the input dimension d, the precision parameter $\varepsilon^{-1}$ or k. Furthermore, there is no coreset construction that succeeds with probability $1-1/n$ and whose size does not depend on the number of input points, n. This has led researchers in the area to ask what is the power of randomness for clustering sketches [Feldman WIREs Data Mining Knowl. Discov’20].Similarly, the best approximation ratio achievable deterministically without a complexity exponential in the dimension are $1+\sqrt{2}$ for k-median [Cohen-Addad, Esfandiari, Mirrokni, Narayanan, STOC’22] and 6.12903 for k-means [Grandoni, Ostrovsky, Rabani, Schulman, Venkat, Inf. Process. Lett.’22]. Those are the best results, even when allowing a complexity FPT in the number of clusters k: this stands in sharp contrast with the $(1+\varepsilon)$-approximation achievable in that case, when allowing randomization.In this paper, we provide deterministic sketches constructions for clustering, whose size bounds are close to the best-known randomized ones. We show how to compute a dimension reduction onto $\varepsilon^{-O(1)} \log k$ dimensions in time $k^{O\left(\varepsilon^{-O(1)}+\log \log k\right)}$ poly $(n d)$, and how to build a coreset of size $O\left(k^{2} \log ^{3} k \varepsilon^{-O(1)}\right)$ in time $2^{\varepsilon^{O(1)} k \log ^{3} k}+k^{O\left(\varepsilon^{-O(1)}+\log \log k\right)}$ poly $(n d)$. In the case where k is small, this answers an open question of [Feldman WIDM’20] and [Munteanu and Schwiegelshohn, Künstliche Intell. ’18] on whether it is possible to efficiently compute coresets deterministically.We also construct a deterministic algorithm for computing $(1+$ $\varepsilon)$-approximation to k-median and k-means in high dimensional Euclidean spaces in time $2^{k^{2} \log ^{3} k / \varepsilon^{O(1)}}$ poly $(n d)$, close to the best randomized complexity of $2^{(k / \varepsilon)^{O(1)}}$ nd (see [Kumar, Sabharwal, Sen, JACM 10] and [Bhattacharya, Jaiswal, Kumar, TCS’18]).Furthermore, our new insights on sketches also yield a randomized coreset construction that uses uniform sampling, that immediately improves over the recent results of [Braverman et al. FOCS ’22] by a factor k. AU - Cohen-Addad, Vincent AU - Saulpic, David AU - Schwiegelshohn, Chris ID - 14768 T2 - 2023 IEEE 64th Annual Symposium on Foundations of Computer Science TI - Deterministic clustering in high dimensional spaces: Sketches and approximation ER - TY - JOUR AB - Over the last two decades, a significant line of work in theoretical algorithms has made progress in solving linear systems of the form Lx=b, where L is the Laplacian matrix of a weighted graph with weights w(i,j)>0 on the edges. The solution x of the linear system can be interpreted as the potentials of an electrical flow in which the resistance on edge (i, j) is 1/w(i, j). Kelner et al. (in: Proceedings of the 45th Annual ACM Symposium on the Theory of Computing, pp 911–920, 2013. https://doi.org/10.1145/2488608.2488724) give a combinatorial, near-linear time algorithm that maintains the Kirchoff Current Law, and gradually enforces the Kirchoff Potential Law by updating flows around cycles (cycle toggling). In this paper, we consider a dual version of the algorithm that maintains the Kirchoff Potential Law, and gradually enforces the Kirchoff Current Law by cut toggling: each iteration updates all potentials on one side of a fundamental cut of a spanning tree by the same amount. We prove that this dual algorithm also runs in a near-linear number of iterations. We show, however, that if we abstract cut toggling as a natural data structure problem, this problem can be reduced to the online vector–matrix-vector problem, which has been conjectured to be difficult for dynamic algorithms (Henzinger et al., in: Proceedings of the 47th Annual ACM Symposium on the Theory of Computing, pp 21–30, 2015. https://doi.org/10.1145/2746539.2746609). The conjecture implies that the data structure does not have an O(n1−ϵ) time algorithm for any ϵ>0, and thus a straightforward implementation of the cut-toggling algorithm requires essentially linear time per iteration. To circumvent the lower bound, we batch update steps, and perform them simultaneously instead of sequentially. An appropriate choice of batching leads to an O˜(m1.5) time cut-toggling algorithm for solving Laplacian systems. Furthermore, we show that if we sparsify the graph and call our algorithm recursively on the Laplacian system implied by batching and sparsifying, we can reduce the running time to O(m1+ϵ) for any ϵ>0. Thus, the dual cut-toggling algorithm can achieve (almost) the same running time as its primal cycle-toggling counterpart. AU - Henzinger, Monika H AU - Jin, Billy AU - Peng, Richard AU - Williamson, David P. ID - 14043 JF - Algorithmica SN - 0178-4617 TI - A combinatorial cut-toggling algorithm for solving Laplacian linear systems VL - 85 ER - TY - CONF AB - We present an auction algorithm using multiplicative instead of constant weight updates to compute a (1−ε)-approximate maximum weight matching (MWM) in a bipartite graph with n vertices and m edges in time O(mε−1log(ε−1)), matching the running time of the linear-time approximation algorithm of Duan and Pettie [JACM ’14]. Our algorithm is very simple and it can be extended to give a dynamic data structure that maintains a (1−ε)-approximate maximum weight matching under (1) one-sided vertex deletions (with incident edges) and (2) one-sided vertex insertions (with incident edges sorted by weight) to the other side. The total time time used is O(mε−1log(ε−1)), where m is the sum of the number of initially existing and inserted edges. AU - Zheng, Da Wei AU - Henzinger, Monika H ID - 13236 SN - 0302-9743 T2 - International Conference on Integer Programming and Combinatorial Optimization TI - Multiplicative auction algorithm for approximate maximum weight bipartite matching VL - 13904 ER -