Pith. sign in

REVIEW 1 cited by

On the sensitivity of the optimal partition for parametric second-order conic 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

arxiv 1910.03684 v2 pith:5VJ6YRS7 submitted 2019-10-08 math.OC

classification math.OC
keywords optimalpartitionconicintervalnonlinearityoptimizationparametricsecond-order
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In this paper, using an optimal partition approach, we study the parametric analysis of a second-order conic optimization problem, where the objective function is perturbed along a fixed direction. We characterize the notions of so-called invariancy set and nonlinearity interval, which serve as stability regions of the optimal partition. We then propose, under the strict complementarity condition, an iterative procedure to compute a nonlinearity interval of the optimal partition. Furthermore, under primal and dual nondegeneracy conditions, we show that a boundary point of a nonlinearity interval can be numerically identified from a nonlinear reformulation of the parametric second-order conic optimization problem. Our theoretical results are supported by numerical experiments.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On computing the nonlinearity interval in parametric semidefinite optimization

    math.OC 2019-08 conditional novelty 6.0 of 10

    The authors show transition points in parametric semidefinite optimization are finite and give a numerical algebraic geometry algorithm to find nonlinearity intervals and transition points.

Pith tools