REVIEW 1 cited by
A survey on combinatorial optimization
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
read the original abstract
This survey revisits classical combinatorial optimization algorithms and extends them to two-stage stochastic models, particularly focusing on client-element problems. We reformulate these problems to optimize element selection under uncertainty and present two key sampling algorithms: SSA and Boost-and-Sample, highlighting their performance guarantees. Additionally, we explore correlation-robust optimization, introducing the concept of the correlation gap, which enables approximations using independent distributions with minimal accuracy loss. This survey analyzes and presents foundational combinatorial optimization methods for researchers at the intersection of this field and reinforcement learning.
Forward citations
Cited by 1 Pith paper
-
Explicit Solution Equation for Every Combinatorial Problem via Tensor Networks: MeLoCoToN
Any finite combinatorial problem with a known logical circuit can be encoded as a tensor network whose contraction defines an explicit, though generally inefficient, solution equation.
Discussion (0). Continue with ORCID to comment.