\[Alg:DADMMGeneral\] there are $C$ groups of nodes, arranged by colors. As in the bipartite case, in each group, the nodes are not neighbors between themselves and operate at the same time. In practice, they cannot operate at the same time because there is no central coordination. As seen in the expression for $v_p^k$ (step \[SubAlg:GeneralDADMMv\]), one node, knowing the color of its neighbors, can operate only after having received the estimates $x_k^{k+1}$ from the nodes with lower color. Excepting that, the algorithm is very similar to Algorithm \[Alg:DADMMGeneral\].

As said before, although there is no convergence guarantee for Algorithm \[Alg:DADMMGeneral\], it converges in practice for several kinds of problems, and we have never seen a nonconvergent example. We point out that, using a reasoning similar to that of the discussion after Theorem \[Teo:BipApplyADMM\], each matrix $B_i^\top$ can be proved to be full column-rank.

Related Algorithms {#Sec:RelatedAlgs}
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We now review some algorithms for solving . We start with describing the performance measure we will use and its practical relevance.

[[**Performance measure: communication steps.**]{}]{} We divide the algorithms that solve  in two kinds: single-looped and double-looped. The single-looped algorithms have just one (nested) loop, and in each iteration, each node receives 