Linesearches vs. fixed stepsize in Optimistix

[bagibence]

By default Optimistix doesn't expose the search attribute of concrete solvers but NeMoS users might want to flexibly switch between linesearches and constant learning rates depending on whether stepsize is passed as a solver argument.

A solution to this would be to create short redefinitions of the required solvers with the search as an argument to __init__, and in the adapter dealing with stepsize with something like:

class BFGS(AbstractBFGS[Y, Aux, _Hessian]):
    rtol: float
    atol: float
    norm: Callable[[PyTree], Scalar]
    use_inverse: bool
    descent: NewtonDescent
    search: AbstractSearch
    verbose: frozenset[str]

    def __init__(
        self,
        rtol: float,
        atol: float,
        norm: Callable[[PyTree], Scalar] = max_norm,
        use_inverse: bool = True,
        verbose: frozenset[str] = frozenset(),
        search: AbstractSearch = Zoom(initial_guess_strategy="one"),
    ):
        self.rtol = rtol
        self.atol = atol
        self.norm = norm
        self.use_inverse = use_inverse
        self.descent = NewtonDescent(linear_solver=lx.Cholesky())
        self.search = search
        self.verbose = verbose

and

if "stepsize" in solver_init_kwargs:
   assert "search" not in solver_init_kwargs, "Specify either search or stepsize"
   solver_init_kwargs["search"] = optx.LearningRate(
       solver_init_kwargs.pop("stepsize")
   )