# JuMP & SumOfSquares generate SDPs with ~18x more constraints than YALMIP

**URL:** <https://discourse.julialang.org/t/jump-sumofsquares-generate-sdps-with-18x-more-constraints-than-yalmip/65377>\
**Category:** Optimization (Mathematical)\
**Tags:** jump\
**Created:** [July 27, 2021, 2:19pm UTC](https://discourse.julialang.org/t/jump-sumofsquares-generate-sdps-with-18x-more-constraints-than-yalmip/65377 "2021-07-27T14:19:36Z")\
**Posts on this page:** 1\
**Showing post:** 12

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**Author:** ![blegat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/blegat/32/217090_2.png) [@blegat](https://discourse.julialang.org/u/blegat)\
**Post date:** [July 29, 2021, 3:28pm UTC](https://discourse.julialang.org/t/jump-sumofsquares-generate-sdps-with-18x-more-constraints-than-yalmip/65377/12 "2021-07-29T15:28:16Z")

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As discussed in [YALMIP vs JuMP - #10 by blegat](https://discourse.julialang.org/t/yalmip-vs-jump/30776/10), YALMIP automatically dualizes the problem before giving it to Mosek. To have the same behavior with JuMP or SumOfSquares, use [https://github.com/jump-dev/Dualization.jl/](https://github.com/jump-dev/Dualization.jl/) and `model = Model(dual_optimizer(Mosek.Optimizer))`.

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