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This work is concerned with solving non-convex power optimization problems by introducing the concept of "nonlinear optimization over graph". To this end, the structure of a given nonlinear real/complex optimization with quadratic arguments is mapped into a generalized weighted graph, where each edge is associated with a weight set constructed from the known parameters of the optimization (e.g., the coefficients). This generalized weighted graph captures both the sparsity of the optimizationdoi:10.1109/camsap.2013.6714098 dblp:conf/camsap/SojoudiL13 fatcat:ut3b5xcuw5gndhiqvor2jj3kma