An ideal-sparse generalized moment problem reformulation for completely positive tensor decomposition exploiting maximal cliques of multi-hypergraphs

Abstract: In this paper, we consider the completely positive tensor decomposition problem with ideal-sparsity. First, we propose an algorithm to generate the maximal cliques of multi-hypergraphs associated with completely positive tensors. This also leads to a necessary condition for tensors to be completely positive. Then, the completely positive tensor decomposition problem is reformulated into an ideal-sparse generalized moment problem. It optimizes over several lower dimensional measure variables supported on the maximal cliques of a multi-hypergraph. The moment-based relaxations are applied to solve the reformulation. The convergence of this ideal-sparse moment hierarchies is studied. Numerical results show that the ideal-sparse problem is faster to compute than the original dense formulation of completely positive tensor decomposition problems. It also illustrates that the new reformulation utilizes sparsity structures that differs from the correlative and term sparsity for completely positive tensor decomposition problems.