Abstract
Current algorithms for low-rank matrix completion often suffer from scalability issues — both in terms of memory as well as running time — when presented with very large datasets. In this paper, we introduce new parallel computing heuristics that can greatly accelerate matrix completion algorithms when used in GPU-based computing environments. Our heuristics enable speeding up popular algorithms for nonlinear matrix completion on standard real-world test datasets by orders of magnitude, while being highly memory-efficient.
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