For generic linear concentration models the maximum likelihood threshold equals the naive dimension count.
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A sparsity condition on the hypergraph of a polynomial allows polynomial-time solution of box-constrained optimization over the unit hypercube by fixing some variables to binary values and locally eliminating others.
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Maximum likelihood thresholds of generic linear concentration models
For generic linear concentration models the maximum likelihood threshold equals the naive dimension count.
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A polynomial-time solvable class of sparse box-constrained polynomial optimization problems
A sparsity condition on the hypergraph of a polynomial allows polynomial-time solution of box-constrained optimization over the unit hypercube by fixing some variables to binary values and locally eliminating others.