Hello, I’m trying to solve MI-NLP problem with SDDP.jl. I would like to use juniper.jl as optimizer: using SDDP using Ipopt using Juniper nl_solver = optimizer_with_attributes(Ipopt.Optimizer, "print_level"=>0) minlp_solver = optimizer_with_attributes(Juniper.Optimizer, "nl_solver"=>nl_solver) …

SDDP.jl isn’t the right tool to use for solving a MINLP. It’s mainly intended for multistage stochastic linear problems. There’s limited support for continuous nonlinear convex problems, for which you can use Ipopt. There’s also some limited support for integer linear problems, for which you should…

whenever there are multiplication, square root or division, constraints or objectives should be posted as nonlinear? One of my constraints is defined as (using SDDP.jl): @NLconstraint( sp, flow == (0.685 - 0.19 * n * 0.05 / (level.in - 261)) * 0.5 * n * sqrt(2 * 9.81 * (level.in -…

You might want to revisit the theory behind SDDP.jl: Introductory theory · SDDP.jl It approximates convex functions, so things like 1 / level.in and sqrt(level.in) or nonlinear equality constraints will not work. SDDP.jl lets you formulate nonlinear models, because in some cases you can formulates…

Thanks! I will not use integer variable in my case.

Despite of SDDP.jl, is there any other package supported to solve multistage dynamic programming and also MINLP? In my case, I don’t really care about stochasticity, the main issue is about multistage.

If you have a deterministic time-staged MINLP, why not just formulate it as a single large MINLP? Time-staged decomposition methods don’t work well for MINLP due to the non-convexity.

Well, I attempt to formulate my problem as a single large MINLP at the beginning. But it seems to be a little complicated, so I turn to SDDP.jl

SDDP: how to use Juniper.jl for optimizer

Specific Domains Optimization (Mathematical)

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SDDP: how to use Juniper.jl for optimizer

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