6 citations · 28 across the 23 of their papers we have counts for
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math.OC2018
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains
Yaqi Duan, Mengdi Wang, Zaiwen Wen +1
This paper develops a low-nonnegative-rank approximation method to identify the state aggregation structure of a finite-state Markov chain under an assumption that the state space…
math.OC2018
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
Jiang Hu, Bo Jiang, Lin Lin +2
In this paper, we study structured quasi-Newton methods for optimization problems with orthogonality constraints. Note that the Riemannian Hessian of the objective function require…
math.OC2018
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
Andre Milzarek, Xiantao Xiao, Shicong Cen +2
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the…