REVIEW 2 cited by
A Doubly Accelerated Inexact Proximal Point Method for Nonconvex Composite Optimization Problems
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
abstract
This paper describes and establishes the iteration-complexity of a doubly accelerated inexact proximal point (D-AIPP) method for solving the nonconvex composite minimization problem whose objective function is of the form $f+h$ where $f$ is a (possibly nonconvex) differentiable function whose gradient is Lipschitz continuous and $h$ is a closed convex function with bounded domain. D-AIPP performs two types of iterations, namely, inner and outer ones. Its outer iterations correspond to the ones of the accelerated inexact proximal point scheme. Its inner iterations are the ones performed by an accelerated composite gradient method for inexactly solving the convex proximal subproblems generated during the outer iterations. Thus, D-AIPP employs both inner and outer accelerations.
Forward citations
Cited by 2 Pith papers
-
Inexact Proximal Point Algorithms for Zeroth-Order Global Optimization
Inexact proximal point methods, with Gibbs-sampling or tensor-train estimates of the proximal operator, converge to the global minimum of nonconvex black-box functions under a gap assumption.
-
Unifying restart accelerated gradient and proximal bundle methods
A restarted accelerated gradient method and the proximal bundle method are both shown to be instances of accelerating and non-accelerating inexact proximal point frameworks, with optimal iteration complexity.
Discussion (0). Continue with ORCID to comment.