Optimization.jl - Define the objective function and its gradient in the same function

Specific Domains Optimization (Mathematical)
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Hi all, I am working on an optimization problem involving Complex Gaussian Processes and Kalman Filter. To optimize the hyperparameters of the kernels, I use Optimization.jl. Because I have calculated the analytical gradients, I can use gradient-based methods.

I define the OptimizationFunction as:

OptimizationFunction(objfun!, grad = objfun_gradient!)

It works, but objfun! and objfun_gradient! share a lot of computations. So, it would be ideal if I can define the objective function and its gradient in the same function in order to save computation time and resources.

My question is: Is it possible to do this with Optimization.jl? My understanding of the documentation is that it is not possible, but a clever solution may exist.

Thanks!

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I think individual solvers like Optim.jl may support it, but it doesn’t seem to be planned for the interface library: see Is there an equivalent to Optim.jl's only_fg! ? · Issue #1101 · SciML/Optimization.jl · GitHub

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@gdalle Thank you for your accurate answer (as usual).