Latest Research in Stochastic Gradient Optimization Techniques

Top Research Topics in Stochastic Gradient Optimization Techniques

• Optimization and Control — 11 papers
• Machine Learning — 1 papers
• Stochastic Gradient Optimization Techniques — 1 papers
• Optimization and Variational Analysis — 1 papers

Highest-Cited Papers

1. A sequential regularized piecewise affine algorithm for nonconvex nonsmooth multicomposite optimization in RNN training
2. Fenchel-Young Duality Gaps: Certified Early Stopping for Regularized Inverse Problems
3. A proximal linearized NEP method for composite optimization with conic and manifold constraints
4. Interior-point proximal methods for nonsmooth optimization in Hilbert spaces with cone-ordered constraints
5. Global Linear Convergence of the Proximal Bundle Method under Unknown Piecewise Smoothness and Quadratic Growth
6. Counterexamples for BFGS-type methods under arbitrary strong Wolfe constants
7. The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
8. Projected Subgradient Methods for a Class of Nonsmooth and Nonconvex Optimization Problems
9. Robust stochastic gradient descent for linearly constrained problems via adaptive barrier amplification
10. A Hybrid Subgradient Method for Nonsmooth Nonconvex Bilevel Optimization
11. Counterexamples to Whole-Sequence Convergence of Variable-Smoothing Full-Splitting Methods
12. A single loop method for quadratic minmax optimization
13. A first-order method for constrained nonconvex-nonconcave minimax optimization
14. A constraint dissolving inexact penalty method for optimization problems with geometric constraints