General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming
classification
🧮 math.OC
keywords
heuristicsconstrainedframeworkgeneralintroducemethodsnonconvexqcqps
read the original abstract
We introduce the Suggest-and-Improve framework for general nonconvex quadratically constrained quadratic programs (QCQPs). Using this framework, we generalize a number of known methods and provide heuristics to get approximate solutions to QCQPs for which no specialized methods are available. We also introduce an open-source Python package QCQP, which implements the heuristics discussed in the paper.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.