to computing a 3D model from (incomplete) distance information. We exploit the resemblance of the objective functions (see Section \[sec:prelim\]), and extend the basic model and algorithm shown in Section \[sub:maxent\] by specifics of the biological application. Our algorithmic adaptations and extensions, as well as details regarding their implementation, are presented in Section \[sec:algo-impl\].

Extensive experiments (see Section \[sec:experiments\]) reveal that our algorithm is significantly faster than other competitive algorithms; at the same time its solution quality is very often better than the results of the best competitor. In particular in our most realistic instances, we outperform our competitors (i) in terms of quality by providing more accurate structural models in (ii) consistently high agreement with reference models and requiring (iii) only about $5$-$10$% of the running time. This stays true even when provided with noisy input data. We further extend our algorithm to support a weighted problem variant that allows to specify how certain a distance interval is and obtain very promising experimental results for this setting as well. To our knowledge, our algorithm is the first to support this new variant.

Preliminaries {#sec:prelim}
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Problem Definition
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We model a biomolecule as a graph $G = (V, E)$, 