Generalization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning
Randomized block coordinate descent (RBCD) is a popular optimization algorithm for many large-scale machine learning problems. The existing theoretical analysis of RBCD mainly focuses on its convergence behavior from an optimization perspective, while its generalization behavior remains less understood for nonconvex problems. In this paper, we establish high-probability generalization guarantees for RBCD with nonconvex objectives. In the general nonconvex setting, we derive bounds on the gradients of both empirical and population risks, requiring only standard smoothness assumptions. With polynomially decaying step sizes, we tune the iteration number to balance optimization and generaliza...
| Authors | Ruixi Hu, Liang Wu |
|---|---|
| Primary category | cs.LG |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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