# Signomial optimization

Hi
My model is in GP form, but I have a negative parameter in the objective function.
I wonder if I can solve it with CVX.
Does CVX solve signomial models?

Thanks A signomial with negative coefficient is not a posynomiial, is not convex, and will not be accepted by CVX.

However, you might be able to call CVX in an iterative fashion to attempt to find a local solution to a signomial program using methods described in section 9.,1 of “A tutorial on geometric programming” by Stephen Boyd, Seung-Jean Kim,·Lieven Vandenberghe, Arash Hassibi, which is available at https://stanford.edu/~boyd/papers/pdf/gp_tutorial.pdf . I recommend you carefully study that tutorial.

Mark is right; if it has a negative coefficient, it is not in GP form. Thanks a lot for responding.
I make my model in signomial GP form with the help of this article http://www.sciencedirect.com/science/article/pii/S0377221713008394
Now I use the ggplab software. But I have a question about it. Can I ask it in this forum? From the abstract
`But some transformation and convexification strategies can be used to convert the original signomial geometric programming problem into a series of standard geometric programming problems that can be solved to reach a global solution.`

Apparently, this is one of several papers over the years which present algorithms which (attempt to) solve the global optimization problem for Signomial GP or other generalized geometric programs by solving a sequence of convex optimization problems.

If these convex optimization problems can be formulated consistent with CVX’s rules, then CVX could be called to solve individual convex problems within the context of a higher level algorithm. I think discussion of how to implement such an algorithm, focusing on the formulation and solution of the convex problems for CVX, could be on-topic for the board.

However, this is not the appropriate forum to ask questions about GGPLAB.