Add conversion to from FastDifferentiation.jl format #953
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| @assert !(typeof(sym_node) <: Symbolics.Arr) "Differentiation of expressions involving arrays and array variables is not yet supported." | ||
| if SymbolicUtils.istree(Symbolics.unwrap(sym_node)) | ||
| @assert !SymbolicUtils.issym(SymbolicUtils.operation(Symbolics.unwrap(sym_node))) "expressions of the form `q(t)` not yet supported." |
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Pretty much all use cases would use a x(t) symbol, but they act the same in most Jacobians as x. Is there a reason why it wouldn't be supported? It would just be a simple conversion.
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I have support for expressions of the form q(t) but there is a bug in the code that causes these types of expressions to sometimes fail so for now it is not allowed. It is in the queue to be fixed.
When there is an FD term dq(t)/dt = q̇(t) what do you want me to return as the value of q̇(t)? Something like this: D(t)(q)?
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Did not understand your answer. If somebody does this in Symbolics:
using Symbolics
@variables t q(t)
fd_jacobian([q],[t])then (when the bug is fixed and FD properly handles forms like q(t)) FD will compute a term q̇. This is represented with a special node type in FD. When q̇ is converted from FD to Symbolics form should it be Differential(t)(q))?
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Differential(t)(q(t))
it's a little weird but in this case, specifically @variables t q(t) binds q to q(t).
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I will need to dynamically construct q(t). Is there a function to do this? I poked around in the Symbolics.jl src and found this
Line 472 in ee3e491
q(t). If it is could you give me an example of how it should be called?
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Maybe something like this?
function f(q::Symbol,t::Symbol)
tmp = @variables $q($t)
return(tmp[1])
end
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| else | ||
| return x * S(T, m - 1, x, y) + y * C(T, m - 1, x, y) | ||
| end | ||
| end |
| The second tuple term will be `differentiation_variables` converted to `FastDifferentiation` form. | ||
| This tuple can be passed to `fd_make_function` to create an efficient executable.""" | ||
| fd_jacobian(symbolics_function::AbstractArray{Symbolics.Num}, differentiation_variables::AbstractVector{Symbolics.Num}; fast_differentiation=false) = remap(FD.jacobian, symbolics_function, differentiation_variables, fast_differentiation) | ||
| export fd_jacobian |
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It'd love for this to replace jacobian, but not until q(t) starts working!
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@brianguenter @shashi curious about the expected timeline for this? This would be very useful in Optimization.jl so I am quite excited about it! I can help out if there's something blocking this that doesn't need an expert of two the packages? |
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@Vaibhavdixit02 @sashi It's waiting on me fixing a bug that requires a detailed understanding of the internals of the FD algorithms, which are not well documented. I wouldn't stop you from trying but it would be a deep dive. I had started on this last week before I went on vacation. Will be getting back into it this week. How long it will take is unpredictable. It's gnarly code and the error only occurs with big complex expression graphs, which makes debugging more difficult and slow. |
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Okay! Thanks for the update 🙌 |
Draft pull request to see if this is along the lines of what you were thinking.