are placed. The algorithm puts some vertices in both subgraphs (overlapping atoms) to later stitch the two embedded subgraphs together. Its authors tested also in the iMDGP setting ([*i.e.,* ]{}supports inexact distances). The results indicate that is able to compute the structure of proteins with very sparse distance data and high noise in s for a protein having atoms. Fang and Toh [@fang2013using] presented some enhancements to for the iMDGP setting by incorporating knowledge about molecule conformations to improve the robustness of . Their experiments show that their changes indeed lead to better solutions (about 50–70%) with the cost of increased running times (also about 50–70%).

MaxEnt-Stress Optimization {#sub:maxent}
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We aim at developing an algorithm for iMDGP (and its extension wiMDGP, see Section \[sub:algo-wiMDGP\]) with a significantly lower running time than previous algorithms and with solutions of good quality. Our main idea is to use an objective function proposed by Gansner [*et al.*]{} [@gansner2013] for planar graph drawing, called maxent-stress (short for *maximal entropy stress*). As the name suggests, it is composed of two parts, a stress part that penalizes deviations from the prescribed distances (with a quadratic penalty, possibly weighted) and an entropy part that penalizes vertices for 