Reg-ASTRO achieves almost sure Õ(ε^{-1.5}) iteration complexity for stochastic nonconvex problems with mean-zero subexponential noise by coupling adaptive sampling with an adaptively regularized local model.
Springer, New York (1999)
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Computational searches maximizing LPS integrals and L3 norms in 3D periodic Navier-Stokes flows found no evidence of singularity formation, but quantified close approaches and transient growth.
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Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization
Reg-ASTRO achieves almost sure Õ(ε^{-1.5}) iteration complexity for stochastic nonconvex problems with mean-zero subexponential noise by coupling adaptive sampling with an adaptively regularized local model.
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The Ladyzhenskaya-Prodi-Serrin Conditions and the Search for Extreme Behavior in 3D Navier-Stokes Flows
Computational searches maximizing LPS integrals and L3 norms in 3D periodic Navier-Stokes flows found no evidence of singularity formation, but quantified close approaches and transient growth.