Gradient-Normalized Smoothness
Algorithms for optimization with approximate Hessians using gradient-normalized smoothness.
Implementation of the gradient-normalized smoothness (GNS) analysis framework for optimization with approximate Hessians. Provides algorithms, oracle functions, Hessian approximation routines, and utilities for convex and non-convex settings. Includes quick-start instructions and examples reproducing paper results.
Optimization
- Technical
- Research papers
- Source code: Lab Github
- Last commit: 2025-11-09
Machine Learning and Optimization Laboratory
Machine Learning and Optimization Laboratory
The Machine Learning and Optimization Laboratory is interested in machine learning, optimization algorithms and text understanding, as well as several application domains.
This page was last edited on 2026-03-03.
This page was last edited on 2026-03-03.