Deterministic Global Optimization

Focusing on geometric branch-and-bound methods, this monograph explores their applications in deterministic global optimization, including Lipschitzian optimization and interval analysis. It introduces a novel convergence rate concept and evaluates various bounding operations from both theoretical and empirical perspectives. The text also extends the prototype algorithm to address multicriteria and mixed combinatorial optimization challenges. Numerical examples, particularly in facility location problems, are provided, along with applications to circle detection in image processing and scheduling issues in three-dimensional space.