Residual Tracking and Stopping for Iterative Random Sketching
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Iterative random sketching (IRS) offers a computationally expedient approach to solving linear systems. However, IRS' benefits can only be realized if the procedure can be appropriately tracked and stopped – otherwise, the algorithm may stop before the desired accuracy is achieved, or it may run longer than necessary. Unfortunately, IRS solvers cannot access the residual norm without undermining their computational efficiency. While iterative random sketching solvers have access to noisy estimates of the residual, such estimates turn out to be insufficient to generate accurate estimates and confidence bounds for the true residual. Thus, in this work, we propose a moving average estimator for the system's residual, and rigorously develop practical uncertainty sets for our estimator. We then demonstrate the accuracy of our methods on a number of linear systems problems.
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