that decomposes the graph into clusters by running the symmetric reverse Cuthill-McKee algorithm on the distance matrix. The subproblems are solved with a semidefinite programming (SDP) formulation. DAFGL is capable of solving the iMDGP. While its solution quality are mostly reasonable, one has to consider that as much as 70% of distances smaller than 6  were provided with added noise. Also, larger instances increase the running time rapidly. The two largest molecules (PDB: 1toa and 1mqq, see Table \[tab:proteins\]) took already s and s with only modest to poor solution quality ([RMSD]{}: $3.2$Å and $9.8$Å) to solve, even though the algorithm makes use of a distributed SDP solver. Leung and Toh [@leung2009sdp] proposed the [^2] algorithm that is an advancement to the DAFGL algorithm by Biswas [*et al.*]{} [@biswas2008distributed]. If the problem is small enough, solves the problem with an SDP approach and refines the obtained solution with regularized gradient descent. Otherwise, splits the graph into two subgraphs and solves the problem recursively. uses the symmetric reverse Cuthill-McKee algorithm to cluster the vertices initially. In a second step tries to minimize the edge cut between different subgraphs by placing a vertex $v$ into the subgraph where most of its neighbors 