arXiv Machine Learning
Projection-Free Multi-level Algorithms for Stochastic Constrained Compositional Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 12
Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
arXiv:2605. 15350v2 Announce Type: replace-cross Abstract: Stochastic compositional optimization minimizes $F(f(x), x)$ over a compact convex set when the inner map $f$ is available only through noisy samples.
arXiv Machine Learning
Oct 3
Optimal Momentum Methods for Stochastic Multilevel Compositional Optimization
The paper studies stochastic multi‑level optimization where the objective is a nested composition of smooth non‑convex functions. It introduces momentum‑based estimators that track function values at each level, achieving an optimal sample complexity of ≠(ε⁻⁴) for finding an ε‑stationary point without relying on average smoothness assumptions. The authors also present a batch‑free variant using first‑order approximations and clipping, and demonstrate the methods on risk‑averse portfolio optimization and hierarchical tilted empirical risk minimization.
arXiv Machine Learning
Oct 12
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints
arXiv:2610. 13787v1 Announce Type: cross Abstract: We present new algorithms for stochastic optimization problems in which the objective is neither smooth nor convex, subject to a convex constraint set.
arXiv Machine Learning
Sep 22
Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
arXiv:2510.11676v2 Announce Type: replace-cross Abstract: We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex fu...
arXiv Machine Learning
Jun 24
Constrained Variable Projection for Structured Problems
arXiv:2606. 23939v1 Announce Type: cross Abstract: Variable projection is a classical technique for separable nonlinear least-squares problems, in which variables that enter linearly are eliminated exactly, yielding a reduced nonlinear problem.
arXiv Machine Learning
Jul 2
Towards Weaker Variance Assumptions for Stochastic Optimization
arXiv:2504. 09951v2 Announce Type: replace-cross Abstract: We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable.