Add more kwargs (opt_alg and verbose) to FSBP operators

Project.toml

@@ -1,7 +1,7 @@
 name = "SummationByPartsOperators"
 uuid = "9f78cca6-572e-554e-b819-917d2f1cf240"
 author = ["Hendrik Ranocha"]
-version = "0.5.63"
+version = "0.5.64"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"

ext/SummationByPartsOperatorsOptimForwardDiffExt.jl

@@ -10,13 +10,14 @@ using SparseArrays: spzeros
 function SummationByPartsOperators.function_space_operator(basis_functions, nodes::Vector{T},
                                                            source::SourceOfCoefficients;
                                                            derivative_order = 1, accuracy_order = 0,
-                                                           options = Options(g_tol = 1e-14, iterations = 10000)) where {T, SourceOfCoefficients}
+                                                           opt_alg = LBFGS(), options = Options(g_tol = 1e-14, iterations = 10000),
+                                                           verbose = false) where {T, SourceOfCoefficients}
 
     if derivative_order != 1
         throw(ArgumentError("Derivative order $derivative_order not implemented."))
     end
     sort!(nodes)
-    weights, D = construct_function_space_operator(basis_functions, nodes, source; options = options)
+    weights, D = construct_function_space_operator(basis_functions, nodes, source; opt_alg = opt_alg, options = options, verbose = verbose)
     return MatrixDerivativeOperator(first(nodes), last(nodes), nodes, weights, D, accuracy_order, source)
 end
 
@@ -98,7 +99,8 @@ end
 
 function construct_function_space_operator(basis_functions, nodes,
                                            ::GlaubitzNordströmÖffner2023;
-                                           options = Options(g_tol = 1e-14, iterations = 10000))
+                                           opt_alg = LBFGS(), options = Options(g_tol = 1e-14, iterations = 10000),
+                                           verbose = false)
     K = length(basis_functions)
     N = length(nodes)
     L = div(N * (N - 1), 2)
@@ -127,7 +129,8 @@ function construct_function_space_operator(basis_functions, nodes,
 
     x0 = zeros(L + N)
     fg!(F, G, x) = optimization_function_and_grad!(F, G, x, p)
-    result = optimize(Optim.only_fg!(fg!), x0, LBFGS(), options)
+    result = optimize(Optim.only_fg!(fg!), x0, opt_alg, options)
+    verbose && display(result)
 
     x = minimizer(result)
     sigma = x[1:L]

src/function_space_operators.jl

@@ -37,16 +37,18 @@ end
 """
     function_space_operator(basis_functions, nodes, source;
                             derivative_order = 1, accuracy_order = 0,
-                            options = Optim.Options(g_tol = 1e-14, iterations = 10000))
+                            opt_alg = Optim.LBFGS(), options = Optim.Options(g_tol = 1e-14, iterations = 10000),
+                            verbose = false)
 
 Construct an operator that represents a first-derivative operator in a function space spanned by
 the `basis_functions`, which is an iterable of functions. The operator is constructed on the
 interval `[x_min, x_max]` with the nodes `nodes`, where `x_min` is taken as the minimal value in
 `nodes` and `x_max` the maximal value. Note that the `nodes` will be sorted internally. The
 `accuracy_order` is the order of the accuracy of the operator, which can optionally be passed,
 but does not have any effect on the operator. The operator is constructed solving an optimization
-problem with Optim.jl. You can specify the options for the optimization problem with the `options`
-argument, see also the [documentation of Optim.jl](https://julianlsolvers.github.io/Optim.jl/stable/user/config/).
+problem with Optim.jl. You can specify the optimization algorithm and options for the optimization problem
+with the keyword arguments `opt_alg` and `options` respectively, see also the
+[documentation of Optim.jl](https://julianlsolvers.github.io/Optim.jl/stable/user/config/)
 
 The operator that is returned follows the general interface. Currently, it is wrapped in a
 [`MatrixDerivativeOperator`](@ref), but this might change in the future.