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math.OC2017
On stochastic and deterministic quasi-Newton methods for non-Strongly convex optimization: Asymptotic convergence and rate analysis
Farzad Yousefian, Angelia Nedić, Uday Shanbhag
Motivated by applications arising from large scale optimization and machine learning, we consider stochastic quasi-Newton (SQN) methods for solving unconstrained convex optimizatio…
math.OC2017★ 4 cited
Self-tuned mirror descent schemes for smooth and nonsmooth high-dimensional stochastic optimization
Nahidsadat Majlesinasab, Farzad Yousefian, Arash Pourhabib
We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective funct…