An adaptive regularization update for Bregman optimizers achieves target sparsity levels from 75% to 99% with faster early convergence and performance matching or exceeding oracle-tuned baselines.
Linearized Bregman iterations for compressed sensing.Mathematics of computation, 78(267):1515–1536
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Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
An adaptive regularization update for Bregman optimizers achieves target sparsity levels from 75% to 99% with faster early convergence and performance matching or exceeding oracle-tuned baselines.