Develops constant-stepsize and auto-conditioned projected gradient methods plus stochastic variants that achieve new iteration complexity bounds for finding approximate stationary points in nonconvex smooth optimization.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
Develops constant-stepsize and auto-conditioned projected gradient methods plus stochastic variants that achieve new iteration complexity bounds for finding approximate stationary points in nonconvex smooth optimization.