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Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization
International Conference on Machine Learning
By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progressdblp:conf/icml/JohnsonG15 fatcat:jvrzyhnqizgddnk2dyp4gmkomu