REVIEW 2 cited by
Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraints
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
In this paper, we focus on the problem of stochastic optimization where the objective function can be written as an expectation function over a closed convex set. We also consider multiple expectation constraints which restrict the domain of the problem. We extend the cooperative stochastic approximation algorithm from Lan and Zhou [2016] to solve the particular problem. We close the gaps in the previous analysis and provide a novel proof technique to show that our algorithm attains the optimal rate of convergence for both optimality gap and constraint violation when the functions are generally convex. We also compare our algorithm empirically to the state-of-the-art and show improved convergence in many situations.
Forward citations
Cited by 2 Pith papers
-
Stochastic Sequential Quadratic Programming for Optimization with Functional Constraints
Stochastic SQP with exact-penalty prox-linear updates attains O(1/ε²) convex and O(1/ε) strongly convex SFO complexity without bounded-gradient assumptions, and VARAS matches unconstrained accelerated finite-sum rates.
-
Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization
An inexact proximal-point penalty algorithm finds ε-stationary points of non-convex constrained problems in O~(ε^{-5/2}) steps with convex constraints and O~(ε^{-3}) to O~(ε^{-4}) steps with non-convex constraints.
Discussion (0). Continue with ORCID to comment.