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We apply a branch-and-bound (B\&B) algorithm to the D-optimality problembased on a convex mixed-integer nonlinear formulation. We discuss possiblemethodologies to accelerate the convergence of the B\&B algorithm, by combiningthe use of different upper bounds, variable-bound tightening inequalities, andlocal-search procedures. Different methodologies to compute the determinant ofa matrix after a rank-one update are investigated to accelerate thelocal-searches. We discuss our findings through numerical experiments withrandomly generated test problem.

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