REVIEW 2 cited by
Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity
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
Signed reviews
read the original abstract
We consider nonconvex constrained optimization problems and propose a new approach to the convergence analysis based on penalty functions. We make use of classical penalty functions in an unconventional way, in that penalty functions only enter in the theoretical analysis of convergence while the algorithm itself is penalty-free. Based on this idea, we are able to establish several new results, including the first general analysis for diminishing stepsize methods in nonconvex, constrained optimization, showing convergence to generalized stationary points, and a complexity study for SQP-type algorithms.
Forward citations
Cited by 2 Pith papers
-
Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization
ConEx, a single-loop primal-dual method with constraint extrapolation, achieves best-known convergence rates for convex functional constrained problems, and a proximal point method achieves O(1/ε) complexity to approx...
-
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization
Under a relative gradient-noise bound plus a local noise condition at saddles, SGD reaches an approximate second-order stationary point in O(1/(μ²τ)) iterations.
Discussion (0). Continue with ORCID to comment.