Showing math.OCShow all
3 papers · 1 filter
math.OC2026
Low-Order Explicit Hessian Imitation Method for Large-Scale Supervised Machine Learning
Yunlang Zhu, Lingjun Guo, Zahra Khatti +4
An algorithm is proposed for solving optimization problems arising in neural network training for supervised learning. The unique feature of the algorithm is the use of an auxiliar…
math.OC2026
A Gradient Sampling Algorithm for Noisy Nonsmooth Nonconvex Optimization
Albert S. Berahas, Frank E. Curtis, Lara Zebiane
An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the g…
math.OC2026
Projected Stochastic Momentum Methods for Nonlinear Equality-Constrained Optimization for Machine Learning
Qi Wang, Christian Piermarini, Yunlang Zhu +1
Two algorithms are proposed, analyzed, and tested for solving continuous optimization problems with nonlinear equality constraints. Each is an extension of a stochastic momentum-ba…