# Ipopt infeasibility on travis-ci

**URL:** <https://discourse.julialang.org/t/ipopt-infeasibility-on-travis-ci/11612>\
**Category:** Optimization (Mathematical)\
**Created:** [June 12, 2018, 2:58pm UTC](https://discourse.julialang.org/t/ipopt-infeasibility-on-travis-ci/11612 "2018-06-12T14:58:20Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [June 13, 2018, 1:05am UTC](https://discourse.julialang.org/t/ipopt-infeasibility-on-travis-ci/11612/2 "2018-06-13T01:05:49Z")

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This could be for any number of reasons. I don’t have any good way of troubleshooting this.

- You may want to try providing a starting solution using the `start` keyword in `@variable`. (See the docs [Nonlinear Modeling — JuMP -- Julia for Mathematical Optimization 0.18 documentation](http://www.juliaopt.org/JuMP.jl/0.18/nlp.html))

- I would also try changing the tolerances in Ipopt.

- If you have any random inputs make sure you set the same seed.

- Your problem might also be poorly scaled. It looks like your objective value is on the order of 10^9. If you have very large and very small coefficients / variable values, it can lead to numeric difficulties with the solvers.

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