I’ve seen many papers (for example, this one on sequential convex programming) where non-convex problems are solved with convex optimization methods by first linearizing the problem at iteration k about the solution given at iteration k-1, and implementing a trust region constraint where ||x^{k}-x^{k-1}||\leq \delta in order to make convergence to a solution more robust. However, I recently tried to use a trust region constraint in a problem that used a sequential convex programming technique, and found that without the trust region constraint it converged to a stable solution, but *with* a trust region constraint, the problem blew up, giving me NaN values after the first iteration. I thought that trust regions were supposed to increase convergence robustness, but it seems that I’ve somehow achieved the opposite result. I’m hoping it’s just a trivial mistake I’ve made somewhere, so thought I’d see if trust regions causing problems to blow up are common with beginners in this field.

# Trust regions cause optimization to blow up

**JeffR**(Jeff) #1

**mcg**(Michael C. Grant) #2

This really isn’t the right forum for general purpose modeling questions. You should consider a more general mathematics or optimization forum, such as Math StackExchange. (Even if you used CVX in the above exercise—well, there’s no *code* here for us to assist with.) Feel free to return with specific CVX usage questions if you have them, of course.