MDGP optimization problem. Yet, for most algorithms the required running time is either very high or the solution quality is in the meantime rather low. Also, some algorithms currently do not support iMDGP—which limits their use in a real-world scenario. Due to space constraints the description of related work focuses on two tools, and , which we use in our experimental comparison as they are publicly available and established in the distance geometry research community – cf. a book on distance geometry edited by Mucherino [*et al.*]{} [@mucherino2012distance] and an even more recent survey by the same authors [@liberti2014survey]. Mentioned running times (in seconds) are based on experimental data in previous works and are thus not necessarily completely comparable. For a broader overview we refer to the aforementioned book and survey.

Liberti [*et al.*]{} [@liberti2014survey] found in their survey from 2014 that general-purpose global optimization solvers like Octave’s *fsolve* or spatial branch-and-bound techniques are not able to solve the problem for more than 10 vertices in a reasonable amount of time. One reason is that the objective function has a large number of local minima.

Moré and Wu [@more1997] implemented an algorithm called [^1] that transforms the objective function gradually 