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Motivated by recent developments in serverless systems for large-scalecomputation as well as improvements in scalable randomized matrix algorithms,we develop OverSketched Newton, a randomized Hessian-based optimizationalgorithm to solve large-scale convex optimization problems in serverlesssystems. OverSketched Newton leverages matrix sketching ideas from RandomizedNumerical Linear Algebra to compute the Hessian approximately. These sketchingmethods lead to inbuilt resiliency against stragglers that are a characteristicof serverless architectures. Depending on whether the problem is stronglyconvex or not, we propose different iteration updates using the approximateHessian. For both cases, we establish convergence guarantees for OverSketchedNewton and empirically validate our results by solving large-scale supervisedlearning problems on real-world datasets. Experiments demonstrate a reductionof ~50% in total running time on AWS Lambda, compared to state-of-the-artdistributed optimization schemes.

OverSketched Newton: Fast Convex Optimization for Serverless Systems