Oscar Dowson

No. Assuming that your inflows are just a function of `ξ`, you'd need to compute them when creating the historical simulator.

I have been talking to the folks at PSR about this. We should modify: https://github.com/odow/SDDP.jl/blob/master/src/plugins/backward_sampling_schemes.jl#L15-L31 to include: `sample_backward_noise_terms_with_state(sampler::MonteCarloSampler, node::Node, state::Dict{Symbol,Float64})` and then we can write a custom backwards pass that...

> Is this the optimal approach? There is no single "optimal" approach. It depends on your model > How can I add weights to nodal transition based on my simulated...

Sure. SDDP.jl doesn't provide tools to help with this. It is up to you to design the graph that is most appropriate for your problem.

Closing because I don't think there is anything left to do here. Please comment if you have further questions and I will re-open.

We'd need to check the finiteness in https://github.com/odow/SDDP.jl/blob/66096558ae9ba652086d954b156b809361c519ee/src/visualization/publication_plot.jl#L16-L17 But I think we should still accept `NaN`. So just `+-Inf`.

As the error says, MibS supports only problems with mixed-integer variables in the lower and upper. It does not support the case where one of the levels contains only continuous...

I can to report this again: https://github.com/odow/SDDP.jl/issues/782#issuecomment-2336825119 This time, we find an `INFEASIBLE_POINT`, but HiGHS declares optimality. ```Julia julia> using JuMP, HiGHS julia> model = JuMP.read_from_file("subproblem_9.mof.json") A JuMP Model ├...

Yes, because the macro cannot rewrite the expressions inside `p.funcs.g`. We considered automatically converting the result, but decided against it. You should modify `p.funcs.g` to create the appropriate expression type.

`@common_expression(model, y, f(x))` ```julia @variable(model, y) @constraint(model, y :== f(x)) ```