TL;DR
This paper analyzes resparsification algorithms for matrices, demonstrating they achieve guarantees comparable to traditional offline sparsifiers, and provides a formal analysis of a scheme similar to Kelner and Levin's proposal.
Contribution
It offers a formal analysis showing that iterative resampling schemes match the guarantees of classic offline matrix sparsifiers, including a scheme akin to Kelner and Levin's.
Findings
Resparsification schemes match classic offline guarantees.
Formal analysis of a scheme similar to Kelner and Levin's.
Provides theoretical validation for iterative resampling methods.
Abstract
We show that schemes for sparsifying matrices based on iteratively resampling rows yield guarantees matching classic 'offline' sparsifiers (see e.g. Spielman and Srivastava [STOC 2008]). In particular, this gives a formal analysis of a scheme very similar to the one proposed by Kelner and Levin [TCS 2013].
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Stochastic Gradient Optimization Techniques · Error Correcting Code Techniques