A new global optimization algorithm for signomial geometric programming via Lagrangian relaxation

摘要

In this paper, a global optimization algorithm, which relies on the exponential variable transformation of the signomial geometric programming (SGP) and the Lagrangian duality of the transformed programming, is proposed for solving the signomial geometric programming (SGP). The difficulty in utilizing Lagrangian duality within a global optimization context is that the restricted Lagrangian function for a given estimate of the Lagrangian multipliers is often nonconvex. Minimizing a linear underestimation of the restricted Lagrangian overcomes this difficulty and facilitates the use of Lagrangian duality within a global optimization framework. In the new algorithm the lower bounds are obtained by minimizing the linear relaxation of restricted Lagrangian function for a given estimate of the Lagrange multipliers. A branch-and-bound algorithm is presented that relies on these Lagrangian relaxations to provide lower bounds and on the interval Newton method to facilitate convergence in the neighborhood of the global solution. Computational results show that the algorithm is efficient.