Theoretical analysis of accelerated gradient methods showing almost-sure escape from strict saddles and linear exit times, plus a subclass achieving near-optimal convergence to local minima in convex neighborhoods of nonconvex functions.
Gradient Descent Converges to Minimizers
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory.
representative citing papers
Permutation symmetries generate permutation saddles and equal-loss valleys linking equivalent global minima, yielding a lower bound on symmetry-induced critical points.
Introduces the SANC algorithm combining negative curvature with stochastic adaptive cubic regularization for nonconvex optimization and claims it is the first such combination with consistent batch sizes for large-scale ML.
citing papers explorer
-
Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima
Theoretical analysis of accelerated gradient methods showing almost-sure escape from strict saddles and linear exit times, plus a subclass achieving near-optimal convergence to local minima in convex neighborhoods of nonconvex functions.