  Signomial geometric programming (SGP) is a computationally challenging,
NP-Hard class of nonconvex nonlinear optimization problems. SGP can be solved
iteratively using a sequence of convex relaxations; consequently, the strength
of such relaxations is an important factor to this iterative approach.
Motivated by recent advances in solving exponential conic programming (ECP)
problems, this paper develops a novel convex relaxation for SGP. Unlike
existing work on relaxations, the base model in this paper does not assume
bounded variables. However, bounded variables or monomial terms can be used to
strengthen the relaxation by means of additional valid linear inequalities. We
show how to embed the ECP relaxation in an iterative algorithm for SGP;
leveraging recent advances in interior point method solvers, our computational
experiments demonstrate the practical effectiveness of this approach.
