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We give a concentration inequality for a stochastic version of the facility location problem. We show the objective Cn = minF[0;1]2 jFj +Px2X minf2F kx fk is concentrated in an interval of length O(n1=6) and E[Cn] = (n2=3) if the input X consists of i.i.d. uniform points in the unit square. Our main tool is to use a geometric quantity, previously used in the design of approximation algorithms for the facility location problem, to analyze a martingale process. Many of our techniques generalize to other settings.more » « less

We explore algorithms and limitations for sparse optimization problems such as sparse linear regression and robust linear regression. The goal of the sparse linear regression problem is to identify a small number of key features, while the goal of the robust linear regression problem is to identify a small number of erroneous measurements. Specifically, the sparse linear regression problem seeks a ksparse vector x ∈ Rd to minimize ‖Ax − b‖2, given an input matrix A ∈ Rn×d and a target vector b ∈ Rn, while the robust linear regression problem seeks a set S that ignores at most k rows and a vector x to minimize ‖(Ax − b)S ‖2. We first show bicriteria, NPhardness of approximation for robust regression building on the work of [OWZ15] which implies a similar result for sparse regression. We further show finegrained hardness of robust regression through a reduction from the minimumweight kclique conjecture. On the positive side, we give an algorithm for robust regression that achieves arbitrarily accurate additive error and uses runtime that closely matches the lower bound from the finegrained hardness result, as well as an algorithm for sparse regression with similar runtime. Both our upper and lower bounds rely on a general reduction from robust linear regression to sparse regression that we introduce. Our algorithms, inspired by the 3SUM problem, use approximate nearest neighbor data structures and may be of independent interest for solving sparse optimization problems. For instance, we demonstrate that our techniques can also be used for the wellstudied sparse PCA problem.more » « less

We consider the question of speeding up classic graph algorithms with machinelearned predictions. In this model, algorithms are furnished with extra advice learned from past or similar instances. Given the additional information, we aim to improve upon the traditional worstcase runtime guarantees. Our contributions are the following: (i) We give a faster algorithm for minimumweight bipartite matching via learned duals, improving the recent result by Dinitz, Im, Lavastida, Moseley and Vassilvitskii (NeurIPS, 2021); (ii) We extend the learned dual approach to the singlesource shortest path problem (with negative edge lengths), achieving an almost linear runtime given sufficiently accurate predictions which improves upon the classic fastest algorithm due to Goldberg (SIAM J. Comput., 1995); (iii) We provide a general reductionbased framework for learningbased graph algorithms, leading to new algorithms for degreeconstrained subgraph and minimumcost 01 flow, based on reductions to bipartite matching and the shortest path problem. Finally, we give a set of general learnability theorems, showing that the predictions required by our algorithms can be efficiently learned in a PAC fashionmore » « less

There has been a flurry of recent literature studying streaming algorithms for which the input stream is chosen adaptively by a blackbox adversary who observes the output of the streaming algorithm at each time step. However, these algorithms fail when the adversary has access to the internal state of the algorithm, rather than just the output of the algorithm. We study streaming algorithms in the whitebox adversarial model, where the stream is chosen adaptively by an adversary who observes the entire internal state of the algorithm at each time step. We show that nontrivial algorithms are still possible. We first give a randomized algorithm for the L1heavy hitters problem that outperforms the optimal deterministic MisraGries algorithm on long streams. If the whitebox adversary is computationally bounded, we use cryptographic techniques to reduce the memory of our L1heavy hitters algorithm even further and to design a number of additional algorithms for graph, string, and linear algebra problems. The existence of such algorithms is surprising, as the streaming algorithm does not even have a secret key in this model, i.e., its state is entirely known to the adversary. One algorithm we design is for estimating the number of distinct elements in a stream with insertions and deletions achieving a multiplicative approximation and sublinear space; such an algorithm is impossible for deterministic algorithms. We also give a general technique that translates any twoplayer deterministic communication lower bound to a lower bound for randomized algorithms robust to a whitebox adversary. In particular, our results show that for all p ≥ 0, there exists a constant Cp > 1 such that any Cpapproximation algorithm for Fp moment estimation in insertiononly streams with a whitebox adversary requires Ω(n) space for a universe of size n. Similarly, there is a constant C > 1 such that any Capproximation algorithm in an insertiononly stream for matrix rank requires Ω(n) space with a whitebox adversary. These results do not contradict our upper bounds since they assume the adversary has unbounded computational power. Our algorithmic results based on cryptography thus show a separation between computationally bounded and unbounded adversaries. Finally, we prove a lower bound of Ω(log n) bits for the fundamental problem of deterministic approximate counting in a stream of 0’s and 1’s, which holds even if we know how many total stream updates we have seen so far at each point in the stream. Such a lower bound for approximate counting with additional information was previously unknown, and in our context, it shows a separation between multiplayer deterministic maximum communication and the whitebox space complexity of a streaming algorithmmore » « less

We propose datadriven onepass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, Hsu et al. (2019a) and Jiang et al. (2020) applied machine learning techniques in other data stream problems, using a trained oracle that can predict certain properties of the stream elements to improve on prior “classical” algorithms that did not use oracles. In this paper, we explore the power of a “heavy edge” oracle in multiple graph edge streaming models. In the adjacency list model, we present a onepass triangle counting algorithm improving upon the previous space upper bounds without such an oracle. In the arbitrary order model, we present algorithms for both triangle and four cycle estimation with fewer passes and the same space complexity as in previous algorithms, and we show several of these bounds are optimal. We analyze our algorithms under several noise models, showing that the algorithms perform well even when the oracle errs. Our methodology expands upon prior work on “classical” streaming algorithms, as previous multipass and random order streaming algorithms can be seen as special cases of our algorithms, where the first pass or random order was used to implement the heavy edge oracle. Lastly, our experiments demonstrate advantages of the proposed method compared to stateoftheart streaming algorithms.more » « less

Random dimensionality reduction is a versatile tool for speeding up algorithms for highdimensional problems. We study its application to two clustering problems: the facility location problem, and the singlelinkage hierarchical clustering problem, which is equivalent to computing the minimum spanning tree. We show that if we project the input pointset 𝑋 onto a random 𝑑=𝑂(𝑑𝑋)dimensional subspace (where 𝑑𝑋 is the doubling dimension of 𝑋), then the optimum facility location cost in the projected space approximates the original cost up to a constant factor. We show an analogous statement for minimum spanning tree, but with the dimension 𝑑 having an extra loglog𝑛 term and the approximation factor being arbitrarily close to 1. Furthermore, we extend these results to approximating solutions instead of just their costs. Lastly, we provide experimental results to validate the quality of solutions and the speedup due to the dimensionality reduction. Unlike several previous papers studying this approach in the context of 𝑘means and 𝑘medians, our dimension bound does not depend on the number of clusters but only on the intrinsic dimensionality of 𝑋.more » « less