11 papers
Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework
Junwen Qiu, Bohao Ma, Andre Milzarek +1
Unit excitation (UE) is a common assumption in stochastic saddle avoidance: the stochastic error must have a uniformly positive component along every direction, in expectation. Thi…
Schattor: Schatten-family methods for deep learning optimization
Bohao Ma, Junyu Zhang, Chuan He
Modern deep learning optimization features heterogeneous parameter structures, noisy gradients, and highly nonconvex landscapes, posing significant challenges for both algorithm de…
Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
Junyu Zhang, Feihong Yang, Jian Wang +2
The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy set using deep reinforcemen…
Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models
Jinbiao Chen, Shuang Jin, Guoyun Zhang +3
Robust optimization (RO) provides a principled framework for decision-making under uncertainty, but its practical use is often limited by the need to manually reformulate uncertain…
Adaptive Newton-CG methods with global and local analysis for unconstrained optimization with Hölder continuous Hessian
Ziyang Zeng, Junyu Zhang, Chuan He
In this paper, we study Newton-conjugate gradient (Newton-CG) methods for minimizing a nonconvex function whose Hessian is -Hölder continuous with modulus an…
Shuffling the Stochastic Mirror Descent via Dual Lipschitz Continuity and Kernel Conditioning
Junwen Qiu, Leilei Mei, Junyu Zhang
The global Lipschitz smoothness condition underlies most convergence and complexity analyses via two key consequences: the descent lemma and the gradient Lipschitz continuity. How…