I am trying to test nonlinear systems solvers, preferably with lots of available algorithms (analogous to the packages Optim.jl and NLopt.jl for optimization). I know of NLsolve.jl, IntervalRootFinding.jl, and NonlinearSolvers.jl. Any others?

NonlinearSolvers.jl wraps everything that we know of in the Julia ecosystem. If it’s missing a method then open an issue and we can wrap that as well. You might want to throw ModelingToolkit.jl into the conversation because its tearing pass will accelerate any of the nonlinear solvers too, so it’s a…

NLSolvers.jl that will be finished and polished once I have time… but I’ve been saying it for a while :grinning_face_with_smiling_eyes: It is tagged, but the interface is not fully stable. It contains code similar to Optim and NLsolve (and the optimization part of LsqFit) in one bundle with relative…

@ChrisRackauckas @pkofod I have been wondering about the need for having to create a “problem” that gets fed into the solver, as opposed to just feed a plain function to the solver like in Optim or NLsolve. Why is that? is to take advantage of multiple dispatch somehow?

I made the issue here : https://github.com/SciML/SciMLNLSolve.jl/issues/7

an OptimizationProblem struct is fully specified in the sense that the way of calculating everything (function, gradient, hessian, jacobian, inplace operations) is defined. if you only pass a function to a solver, you pass that work on deciding how to calculate everything on to the solver itself, an…

But you can pass custom gradients, to Optim for example is the second positional argument.

You can also pass the gradient to an OptimizationFunction constructor, the customization options are the same between arguments, but, looking at the dispatches in optim at least, it complicates the code a lot for little benefit over just passing an object with all the relevant information. For one e…

I think the approach in Optim is quite user friend if you just want to optimize(f, x) but there are a couple of problems as a package developer. A lot of bug fixes to Optim has been to fix confusing error when a specific combination of f, g, fg, fgh, h, x, lower bounds, upper bounds, algorithm, opt…

There’s multiple reasons for the split. And note that for the simple cases Julia will remove the struct at the compiler level anyways so it’s not a hit on performance either. So the reason to do it is: You need to do this internally anyways, since you want to just throw one bundle of the model aro…

[image] pkofod: I would appreciate any test cases you might have. Here is one such test case: [image] Help optimizing IPFP algorithm Performance I’m trying to speed up this version of the IPFP algorithm, which alternates between solving 2 systems of nonlinear equat…

What nonlinear solvers are available?

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What nonlinear solvers are available?

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