Branch-and-bound for integer D-Optimality with fast local search and variable-bound tightening

TL;DR

This paper introduces a branch-and-bound algorithm for the integer D-optimality problem, utilizing convex relaxations, variable-bound tightening, and fast local search to improve solution efficiency in statistical design.

Contribution

The paper presents a novel branch-and-bound method incorporating convex relaxations and local search for integer D-optimality, enhancing computational performance.

Findings

Effective in solving various test problems

Improved solution speed over existing methods

Demonstrates the benefit of variable-bound tightening

Abstract

We develop a branch-and-bound algorithm for the integer D-optimality problem, a central problem in statistical design theory, based on two convex relaxations, employing variable-bound tightening and fast local-search procedures, testing our ideas on various test problems.