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math.OC2022★ 1 cited
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity
Sally Dong, Haotian Jiang, Yin Tat Lee +2
Many fundamental problems in machine learning can be formulated by the convex program \[ \min_{θ\in R^d}\ \sum_{i=1}^{n}f_{i}(θ), \] where each is a convex, Lipschitz functio…
math.OC2022★ 1 cited
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data
Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan +1
Nonnegative (linear) least square problems are a fundamental class of problems that is well-studied in statistical learning and for which solvers have been implemented in many of t…
math.OC2021★ 3 cited
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee +2
Zhang et al. introduced a novel modification of Goldstein's classical subgradient method, with an efficiency guarantee of for minimizing Lipschitz functions.…