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Electronic Colloquium on Computational Complexity

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Reports tagged with Streaming Algorithms:
TR09-119 | 17th November 2009
Frederic Magniez, Claire Mathieu, Ashwin Nayak

Recognizing well-parenthesized expressions in the streaming model

Motivated by a concrete problem and with the goal of understanding the sense in which the complexity of streaming algorithms is related to the complexity of formal languages, we investigate the problem Dyck(s) of checking matching parentheses, with $s$ different types of parenthesis.

We present a one-pass randomized streaming ... more >>>

TR12-128 | 21st September 2012
Nathanaël François, Frederic Magniez

Streaming Complexity of Checking Priority Queues

Revisions: 1

This work is in the line of designing efficient checkers for testing the reliability of some massive data structures. Given a sequential access to the insert/extract operations on such a structure, one would like to decide, a posteriori only, if it corresponds to the evolution of a reliable structure. In ... more >>>

TR14-180 | 22nd December 2014
Anna Gal, Jing-Tang Jang, Nutan Limaye, Meena Mahajan, Karteek Sreenivasaiah

Space-Efficient Approximations for Subset Sum

SUBSET SUM is a well known NP-complete problem:
given $t \in Z^{+}$ and a set $S$ of $m$ positive integers, output YES if and only if there is a subset $S^\prime \subseteq S$ such that the sum of all numbers in $S^\prime$ equals $t$. The problem and its search ... more >>>

TR15-039 | 16th March 2015
Anup Rao, Makrand Sinha

On Parallelizing Streaming Algorithms

We study the complexity of parallelizing streaming algorithms (or equivalently, branching programs). If $M(f)$ denotes the minimum average memory required to compute a function $f(x_1,x_2, \dots, x_n)$ how much memory is required to compute $f$ on $k$ independent streams that arrive in parallel? We show that when the inputs (updates) ... more >>>

TR15-104 | 13th May 2015
Nathanaël François, Frederic Magniez, Olivier Serre, Michel de Rougemont

Streaming Property Testing of Visibly Pushdown Languages

Revisions: 2

In the context of language recognition, we demonstrate the superiority of streaming property testers against streaming algorithms and property testers, when they are not combined. Initiated by Feigenbaum et al, a streaming property tester is a streaming algorithm recognizing a language under the property testing approximation: it must distinguish inputs ... more >>>

TR15-156 | 21st September 2015
Tim Roughgarden

Communication Complexity (for Algorithm Designers)

This document collects the lecture notes from my course ``Communication Complexity (for Algorithm Designers),'' taught at
Stanford in the winter quarter of 2015. The two primary goals of the course are:

1. Learn several canonical problems that have proved the most useful for proving lower bounds (Disjointness, Index, Gap-Hamming, etc.). ... more >>>

TR16-150 | 23rd September 2016
Lucas Boczkowski, Iordanis Kerenidis, Frederic Magniez

Streaming Communication Protocols

We define the Streaming Communication model that combines the main aspects of communication complexity and streaming. We consider two agents that want to compute some function that depends on inputs that are distributed to each agent. The inputs arrive as data streams and each agent has a bounded memory. Agents ... more >>>

TR18-169 | 18th September 2018
Kaave Hosseini, Shachar Lovett, Grigory Yaroslavtsev

Optimality of Linear Sketching under Modular Updates

We study the relation between streaming algorithms and linear sketching algorithms, in the context of binary updates. We show that for inputs in $n$ dimensions,
the existence of efficient streaming algorithms which can process $\Omega(n^2)$ updates implies efficient linear sketching algorithms with comparable cost.
This improves upon the previous work ... more >>>

TR19-177 | 6th December 2019
Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David Woodruff

Pseudo-deterministic Streaming

A pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple times on the same input, with high probability outputs the same result on all executions. Classic streaming algorithms, such as those for finding heavy hitters, approximate counting, $\ell_2$ approximation, finding a nonzero entry in a vector (for turnstile algorithms) ... more >>>

TR21-011 | 13th February 2021
Chi-Ning Chou, Alexander Golovnev, Madhu Sudan, Santhoshini Velusamy

Classification of the streaming approximability of Boolean CSPs

Revisions: 3 , Comments: 1

A Boolean constraint satisfaction problem (CSP), Max-CSP$(f)$, is a maximization problem specified by a constraint $f:\{-1,1\}^k\to\{0,1\}$. An instance of the problem consists of $m$ constraint applications on $n$ Boolean variables, where each constraint application applies the constraint to $k$ literals chosen from the $n$ variables and their negations. The goal ... more >>>

TR21-063 | 3rd May 2021
Chi-Ning Chou, Alexander Golovnev, Madhu Sudan, Santhoshini Velusamy

Approximability of all finite CSPs in the dynamic streaming setting

Revisions: 2

A constraint satisfaction problem (CSP), Max-CSP$({\cal F})$, is specified by a finite set of constraints ${\cal F} \subseteq \{[q]^k \to \{0,1\}\}$ for positive integers $q$ and $k$. An instance of the problem on $n$ variables is given by $m$ applications of constraints from ${\cal F}$ to subsequences of the $n$ ... more >>>

TR21-086 | 22nd June 2021
Chi-Ning Chou, Alexander Golovnev, Madhu Sudan, Ameya Velingker, Santhoshini Velusamy

Linear Space Streaming Lower Bounds for Approximating CSPs

We consider the approximability of constraint satisfaction problems in the streaming setting. For every constraint satisfaction problem (CSP) on $n$ variables taking values in $\{0,\ldots,q-1\}$, we prove that improving over the trivial approximability by a factor of $q$ requires $\Omega(n)$ space even on instances with $O(n)$ constraints. We also identify ... more >>>

TR21-096 | 8th July 2021
Boaz Menuhin, Moni Naor

Keep That Card in Mind: Card Guessing with Limited Memory

A card guessing game is played between two players, Guesser and Dealer. At the beginning of the game, the Dealer holds a deck of $n$ cards (labeled $1, ..., n$). For $n$ turns, the Dealer draws a card from the deck, the Guesser guesses which card was drawn, and then ... more >>>

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