Branch-and-bound for D-Optimality with fast local search and variable-bound tightening
Optimization and Control
2023-02-16 v1
Abstract
We apply a branch-and-bound (B\&B) algorithm to the D-optimality problem based on a convex mixed-integer nonlinear formulation. We discuss possible methodologies to accelerate the convergence of the B\&B algorithm, by combining the use of different upper bounds, variable-bound tightening inequalities, and local-search procedures. Different methodologies to compute the determinant of a matrix after a rank-one update are investigated to accelerate the local-searches. We discuss our findings through numerical experiments with randomly generated test problem.
Cite
@article{arxiv.2302.07386,
title = {Branch-and-bound for D-Optimality with fast local search and variable-bound tightening},
author = {Gabriel Ponte and Marcia Fampa and Jon Lee},
journal= {arXiv preprint arXiv:2302.07386},
year = {2023}
}