3 papers
math.OC2026
Primal-Dual Inexact Newton-MR for Nonconvex Optimization with Equality Constraints
Oscar Smee, Fred Roosta
Optimization problems with nonlinear equality constraints arise throughout science, engineering, and increasingly in machine learning. Prominent methods for solving such problems i…
math.OC2025
First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
Oscar Smee, Fred Roosta, Stephen J. Wright
Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate.…
math.OC2024
Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints
Oscar Smee, Fred Roosta
We consider solving large scale nonconvex optimisation problems with nonnegativity constraints. Such problems arise frequently in machine learning, such as nonnegative least-square…