Local LMO is a new projection-free method that achieves the convergence rates of projected gradient descent for constrained optimization by using local linear minimization oracles over small balls.
The ball-proximal (=” broximal”) point method: a new al- gorithm, convergence theory, and applications.arXiv preprint arXiv:2502.02002
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Introduces the Riemannian ball-proximal point method (RB-PPM) that minimizes geodesically convex functions over metric balls on Hadamard manifolds and proves quasi-Fejér monotonicity, finite termination under constant radii, and convergence when the sum of radii diverges.
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
A trust-region stabilized proximal point method enforces a displacement condition to achieve linear descent for general nonsmooth convex problems.
A smoothing moving balls approximation method is proposed for difference-of-convex optimization over nonlinear conic constraints, with iteration complexity for approximate KKT points and convergence analysis in the convex case.
citing papers explorer
-
Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
Local LMO is a new projection-free method that achieves the convergence rates of projected gradient descent for constrained optimization by using local linear minimization oracles over small balls.
-
Ball-proximal point method on a Hadamard Manifolds
Introduces the Riemannian ball-proximal point method (RB-PPM) that minimizes geodesically convex functions over metric balls on Hadamard manifolds and proves quasi-Fejér monotonicity, finite termination under constant radii, and convergence when the sum of radii diverges.
-
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
-
Stabilized Proximal Point Method via Trust Region Control
A trust-region stabilized proximal point method enforces a displacement condition to achieve linear descent for general nonsmooth convex problems.
-
A smoothing moving balls approximation method for a class of conic-constrained difference-of-convex optimization problems
A smoothing moving balls approximation method is proposed for difference-of-convex optimization over nonlinear conic constraints, with iteration complexity for approximate KKT points and convergence analysis in the convex case.