efficient and effective method to compute a full structural 3D model from incomplete and/or noisy distances. For this task, physics-based approaches are computationally prohibitive. Molecular dynamics-based approaches require weeks on supercomputers [@lange2008] and stochastic global optimization techniques [@schug2003] still days on medium-sized clusters. To lower computational costs, one can use more coarse-grained force-fields [@rohl2004], yet the computations still require hours to days depending on the input size.

For interactive or nearly-interactive work, however, running times of a few seconds would be desirable. This would support a quick back-and-forth between, [*e.g.,* ]{}assigning NMR chemical shifts to determine pairwise atomic distances and follow-up structural modeling[@stothers2012carbon]. These structural models allow then, in turn, improved NMR shift assignment and a repetition of the loop until no further improvement can be found. One forgoes describing the detailed physics and finds a near-optimal solution which respects all distance constraints. Exemplary tools which solve this distance geometry problem are  [@more1999] and  [@martin2011]. Challenges for such tools are efficiency, solution quality, and support for variations of the original problem such as the ability to deal with noise, error, or distance intervals.

**Outline and Contribution.** In this paper we transfer a maxent-stress optimization approach from 2D graph drawing [@gansner2013] 