Research map / Optimization / Nonconvex and smooth optimization
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace proximal SGD with variance-reduced proximal SVRG/SAGA using constant minibatches | nonconvex nonsmooth finite-sum optimization · NN-PCA (rcv1, a9a, mnist, aloi) | objective suboptimality vs effective passes | Helped |
| use minibatch b=n^{2/3} in PROXSVRG/PROXSAGA rather than b=1 | PROXSVRG and PROXSAGA · theory, IFO complexity | IFO complexity to eps-accuracy: O(n/eps) to O(n + n^{2/3}/eps) | Helped |
| use constant minibatch b=1 in PROXSVRG (Theorem 1) (ablation) | PROXSVRG · theory, IFO and PO complexity | IFO and PO complexity: O(n/eps) same as PROXGD IFO; worse PO | No clear effect |
| add PL-condition restarts (PL-SVRG/PL-SAGA) for mu-PL functions | PROXSVRG and PROXSAGA · theory, PL functions | IFO complexity for linear convergence: O(n log(1/eps)) of PROXGD to O((n+n^{2/3}) log(1/eps)) | Helped |
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