REVIEW 2 cited by
Smoothed Proximal Lagrangian Method for Nonlinear Constrained Programs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Smoothed Proximal Lagrangian Method for Nonlinear Constrained Programs
read the original abstract
This paper introduces a smoothed proximal Lagrangian method for minimizing a nonconvex smooth function over a convex domain with additional explicit convex nonlinear constraints. Two key features are 1) the proposed method is single-looped, and 2) an first-order iteration complexity of $\mathcal{O}(\epsilon^{-2})$ is established under mild regularity assumptions. The first feature suggests the practical efficiency of the proposed method, while the second feature highlights its theoretical superiority. Numerical experiments on various problem scales demonstrate the advantages of the proposed method in terms of speed and solution quality.
Forward citations
Cited by 2 Pith papers
-
Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity
Under local conic regularity, a smoothed prox-linear ALM with dual truncation attains O(K^{-1/3}) KKT rates with dual regularization and O(K^{-1/2}) without it under piecewise-linear outer structure.
-
Stochastic Penalty-Barrier Methods for Constrained Machine Learning
SPBM extends classical penalty-barrier methods to stochastic non-convex non-smooth settings via exponential dual averaging and Moreau envelopes, matching baselines with linear overhead up to 10,000 constraints.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.