# solving convex MINLP problem

**URL:** <https://discourse.julialang.org/t/solving-convex-minlp-problem/18015>\
**Category:** Optimization (Mathematical)\
**Created:** [November 26, 2018, 2:36pm UTC](https://discourse.julialang.org/t/solving-convex-minlp-problem/18015 "2018-11-26T14:36:19Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Jay\_Dave](https://avatars.discourse-cdn.com/v4/letter/j/58956e/32.png) [@Jay\_Dave](https://discourse.julialang.org/u/Jay_Dave)\
**Post date:** [November 26, 2018, 2:36pm UTC](https://discourse.julialang.org/t/solving-convex-minlp-problem/18015/1 "2018-11-26T14:36:19Z")

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Hello,

I am using second-order cone relaxation to convert my original non-linear problem. When I solve it using juniper (cplex as mip+ ipopt ), the solved objective is correct but takes longer than solving the non-linear problem itself for many test cases. However, I realized that for my convex MINLP problem I should use pavito (cplex as mip and ipopt as continuous) . But, my objective value is not even as good as the linearized problem. I am not sure what to make out of this. Do you think the formulation itself has an issue? Was my former way correct- using juniper to solve convex MINLP? I have very limited knowledge of the solvers.