7 citations · 9 across the 2 of their papers we have counts for
2 papers
math.OC2020★ 2 cited
Cubic Regularized Newton Method for Saddle Point Models: a Global and Local Convergence Analysis
Kevin Huang, Junyu Zhang, Shuzhong Zhang
In this paper, we propose a cubic regularized Newton (CRN) method for solving convex-concave saddle point problems (SPP). At each iteration, a cubic regularized saddle point subpro…
math.OC2019★ 7 cited
From low probability to high confidence in stochastic convex optimization
Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao +1
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced hi…