Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems

12/19/2019
by   Julie Sliwak, et al.
0

Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, we experiment several clique decomposition algorithms that lead to different reformulations and we show that the resolution is highly sensitive to the clique decomposition procedure. Our main contribution is to demonstrate that minimizing the number of additional edges in the chordal extension is not always appropriate to get a good clique decomposition.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/17/2019

Revisiting the Graph Isomorphism Problem with Semidefinite Programming

We present a new algorithm for the graph isomorphism problem which solve...
research
03/25/2021

A Semidefinite Optimization-based Branch-and-Bound Algorithm for Several Reactive Optimal Power Flow Problems

The Reactive Optimal Power Flow (ROPF) problem consists in computing an ...
research
07/29/2023

Listing Cliques from Smaller Cliques

We study finding and listing k-cliques in a graph, for constant k≥ 3, a ...
research
12/17/2020

Clique Is Hard on Average for Regular Resolution

We prove that for k ≪√(n) regular resolution requires length n^Ω(k) to e...
research
06/01/2021

Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition

We present a new combinatorial model for identifying regulatory modules ...
research
07/14/2021

Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time

We consider the problem of sampling and approximately counting an arbitr...
research
08/19/2020

Matchings, hypergraphs, association schemes, and semidefinite optimization

We utilize association schemes to analyze the quality of semidefinite pr...

Please sign up or login with your details

Forgot password? Click here to reset