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**Published in:** LIPIcs, Volume 60, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)

An important challenge in the streaming model is to maintain small-space approximations of entrywise functions performed on a matrix that is generated by the outer product of two vectors given as a stream. In other works, streams typically define matrices in a standard way via a sequence of updates, as in the
work of Woodruff [22] and others. We describe the matrix formed by the outer product, and other matrices that do not fall into this category, as implicit matrices. As such, we consider the general problem of computing over such implicit matrices with Hadamard functions, which are functions applied entrywise on a matrix. In this paper, we apply this generalization to provide new techniques for identifying independence between two data streams. The previous state of the art algorithm of Braverman and Ostrovsky [9] gave a (1 +- epsilon)-approximation for the L_1 distance between the joint and product of the marginal distributions, using space O(log^{1024}(nm) epsilon^{-1024}), where m is the length of the stream and n denotes the size of the universe from which stream elements are drawn. Our general techniques include the L_1 distance as a special case, and we give an improved space bound of O(log^{12}(n) log^{2}({nm}/epsilon) epsilon^{-7}).

Vladimir Braverman, Alan Roytman, and Gregory Vorsanger. Approximating Subadditive Hadamard Functions on Implicit Matrices. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 60, pp. 25:1-25:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{braverman_et_al:LIPIcs.APPROX-RANDOM.2016.25, author = {Braverman, Vladimir and Roytman, Alan and Vorsanger, Gregory}, title = {{Approximating Subadditive Hadamard Functions on Implicit Matrices}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)}, pages = {25:1--25:19}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-018-7}, ISSN = {1868-8969}, year = {2016}, volume = {60}, editor = {Jansen, Klaus and Mathieu, Claire and Rolim, Jos\'{e} D. P. and Umans, Chris}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2016.25}, URN = {urn:nbn:de:0030-drops-66483}, doi = {10.4230/LIPIcs.APPROX-RANDOM.2016.25}, annote = {Keywords: Streaming Algorithms, Measuring Independence, Hadamard Functions, Implicit Matrices} }

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**Published in:** LIPIcs, Volume 28, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014)

In this paper, we provide the first optimal algorithm for the remaining open question from the seminal paper of Alon, Matias, and Szegedy: approximating large frequency moments. We give an upper bound on the space required to find a k-th frequency moment of O(n^(1-2/k)) bits that matches, up to a constant factor, the lower bound of Woodruff et. al for constant epsilon and constant k.
Our algorithm makes a single pass over the stream and works for any constant k > 3. It is based upon two major technical accomplishments: first, we provide an optimal algorithm for finding the heavy elements in a stream; and second, we provide a technique using Martingale Sketches which gives an optimal reduction of the large frequency moment problem to the all heavy elements problem. We also provide a polylogarithmic improvement for frequency moments, frequency based functions, spatial data streams, and measuring independence of data sets.

Vladimir Braverman, Jonathan Katzman, Charles Seidell, and Gregory Vorsanger. An Optimal Algorithm for Large Frequency Moments Using O(n^(1-2/k)) Bits. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 28, pp. 531-544, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{braverman_et_al:LIPIcs.APPROX-RANDOM.2014.531, author = {Braverman, Vladimir and Katzman, Jonathan and Seidell, Charles and Vorsanger, Gregory}, title = {{An Optimal Algorithm for Large Frequency Moments Using O(n^(1-2/k)) Bits}}, booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2014)}, pages = {531--544}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-74-3}, ISSN = {1868-8969}, year = {2014}, volume = {28}, editor = {Jansen, Klaus and Rolim, Jos\'{e} and Devanur, Nikhil R. and Moore, Cristopher}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2014.531}, URN = {urn:nbn:de:0030-drops-47217}, doi = {10.4230/LIPIcs.APPROX-RANDOM.2014.531}, annote = {Keywords: Streaming Algorithms, Randomized Algorithms, Frequency Moments, Heavy Hitters} }

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