Hello Tiago. There is the example code which cause the same infinite looping:

*import numpy as np* *from graph_tool.all import ** *gg = collection.data['football']* *dummy_type = np.array(map(lambda x: int('state' in x.lower()), gg.vp.label.get_2d_array([0])[0]))* *dummy_weights = gg.new_edge_property('double')* *dummy_weights.a = edge_endpoint_property(gg, gg.vp.value, "source").a - edge_endpoint_property(gg, gg.vp.value, "target").a* *test = minimize_nested_blockmodel_dl(gg, deg_corr=False, b_min = dummy_type, B_max = 50, B_min=5,* * state_args=dict(clabel = dummy_type, * * recs = [dummy_weights],* * rec_types = ['real-normal']* * ), ** verbose=True)*

My investigation showed that the issue occurs only if I have simultaneously two things: *clabel constraint* and *real-normal covariates* for edges. If I remove one of them - everything is fine, If I change the covariates type from normal to any other -- again, no looping. This specific *clabel* forces for bisection search degenerated bounds (min_state = max_state), and normal covariates some how affects on entropy calculus. As I understand, the problem is in potentially wrong entropy calculation here: https://git.skewed.de/count0/graph-tool/blob/master/src/graph_tool/inference... As we saw from outputs, code is trying to replace (N=2, B=1) with (N=2, B=2) and is getting lower entropy. Here https://git.skewed.de/count0/graph-tool/blob/master/src/graph_tool/inference... I found that only in case of normal covariates you have subtraction for entropy, hence potentially smaller entropy for more sophisticated model. Hope, it helps. Thank you, Valeriy.