A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

Haizhou Liu; Hongbin Sun; Lun Yang; Mohammad Shahidehpour; Qinglai Guo; Xinwei Shen

REVIEW

A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2012.10125 v1 pith:3VOQ3VWZ submitted 2020-12-18 math.OC cs.SYeess.SY

A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

Haizhou Liu , Lun Yang , Xinwei Shen , Qinglai Guo , Hongbin Sun , Mohammad Shahidehpour This is my paper

classification math.OC cs.SYeess.SY

keywords approachconvexdata-drivenflowoptimalstartwarmalgorithm

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

In this letter, we propose a data-driven warm start approach, empowered by artificial neural networks, to boost the efficiency of convex relaxations in optimal gas flow. Case studies show that this approach significantly decreases the number of iterations for the convex-concave procedure algorithm, and optimality and feasibility of the solution can still be guaranteed.

A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

discussion (0)