Parallel branch and bound for multidimensional scaling with city‐block distances

Multidimensional scaling is a technique for exploratory analysis of multidimensional data. The essential part of the technique is minimization of a multimodal function with unfavorable properties like invariants and non‐differentiability. Recently a branch and bound algorithm for multidimensional scaling with city‐block distances has been proposed for solution of medium‐size problems exactly. The algorithm exploits piecewise quadratic structure of the objective function. In this paper a parallel version of the branch and bound algorithm for multidimensional scaling with city‐block distances has been proposed and investigated. Parallel computing enabled solution of larger problems what was not feasible with the sequential version.