On deterministic sketching and streaming for sparse recovery and norm estimation

Abstract

We study classic streaming and sparse recovery problems using deterministic linear sketches, including ℓ1/ℓ1 and ℓ∞/ℓ1 sparse recovery problems (the latter also being known as ℓ1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A ∈ℝm×n and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: • A proof that ℓ∞/ℓ1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(ε-2min{logn,(logn/log(1/ε))2}), which holds for any 0<ε<1/2. We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. • A new lower bound for the number of linear measurements required to solve ℓ1/ℓ1 sparse recovery. We show Ω(k/ε2+klog(n/k)/ε) measurements are required to recover an x' with ∥x-x'∥1≤(1+ε) ∥xtail(k)∥1, where xtail(k) is x projected onto all but its largest k coordinates in magnitude. • A tight bound of m=Θ(ε-2log(ε2n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover ∥-x∥2±ε∥x∥1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of ℓ1/ℓ1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems. We remark that some of the matrices used in our algorithms, although known to exist, currently are not yet explicit in the sense that deterministic polynomial time constructions are not yet known, although in all cases polynomial time Monte Carlo algorithms are known. © 2013 Elsevier Inc. All rights reserved.