Explicit imports #646
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Explicit imports #646
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| ```@example diagnostics_example | ||
| cache = init(prob, DFSane(); show_trace = Val(true), trace_level = TraceMinimal(50)); | ||
| solve!(cache) | ||
| cache = NLS.init(prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50)); |
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| cache = NLS.init(prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50)); | |
| cache = NLS.init( | |
| prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50)); |
| u0 = CUDA.cu(ones(1000)) | ||
| p = CUDA.cu(collect(1:1000)) | ||
| prob = NLS.NonlinearProblem(f, u0, p) | ||
| sol = NLS.solve(prob, NLS.NewtonRaphson(), abstol=1f-4) |
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| sol = NLS.solve(prob, NLS.NewtonRaphson(), abstol=1f-4) | |
| sol = NLS.solve(prob, NLS.NewtonRaphson(), abstol = 1.0f-4) |
| import Metal # For if you have a Mac M-series device and want to use the built-in GPU | ||
| import OneAPI # For if you have an Intel GPU | ||
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| @KernelAbstractions.kernel function parallel_nonlinearsolve_kernel!(result, @Const(prob), @Const(alg)) |
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| @KernelAbstractions.kernel function parallel_nonlinearsolve_kernel!(result, @Const(prob), @Const(alg)) | |
| KernelAbstractions.@kernel function parallel_nonlinearsolve_kernel!( | |
| result, @Const(prob), @Const(alg)) |
| out3 = (x[2] - p[3] * x[3])^2 | ||
| out4 = sqrt(p[4]) * (x[1] - x[4]) * (x[1] - x[4]) | ||
| SA[out1,out2,out3,out4] | ||
| StaticArrays.SA[out1,out2,out3,out4] |
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| StaticArrays.SA[out1,out2,out3,out4] | |
| StaticArrays.SA[out1, out2, out3, out4] |
| p = @SVector [@SVector(rand(Float32, 4)) for _ in 1:1024] | ||
| u0 = SA[1f0, 2f0, 3f0, 4f0] | ||
| p = StaticArrays.@SVector [StaticArrays.@SVector(rand(Float32, 4)) for _ in 1:1024] | ||
| u0 = StaticArrays.SA[1f0, 2f0, 3f0, 4f0] |
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| u0 = StaticArrays.SA[1f0, 2f0, 3f0, 4f0] | |
| u0 = StaticArrays.SA[1.0f0, 2.0f0, 3.0f0, 4.0f0] |
| p_init = zeros(4) | ||
| nlls_prob = NonlinearLeastSquaresProblem(loss_function, p_init, vec(reduce(hcat, sol.u))) | ||
| nlls_prob = NLS.NonlinearLeastSquaresProblem(loss_function, p_init, vec(reduce(hcat, sol.u))) |
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| nlls_prob = NLS.NonlinearLeastSquaresProblem(loss_function, p_init, vec(reduce(hcat, sol.u))) | |
| nlls_prob = NLS.NonlinearLeastSquaresProblem( | |
| loss_function, p_init, vec(reduce(hcat, sol.u))) |
| ```@example parameterized_ode | ||
| res = solve(nlls_prob, LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true), | ||
| trace_level = TraceWithJacobianConditionNumber(25)) | ||
| res = NLS.solve(nlls_prob, NLS.LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true), |
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| res = NLS.solve(nlls_prob, NLS.LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true), | |
| res = NLS.solve( | |
| nlls_prob, NLS.LevenbergMarquardt(); maxiters = 1000, show_trace = Val(true), |
| nlfunc_dense = NonlinearFunction(form_residual!) | ||
| nlfunc_sparse = NonlinearFunction(form_residual!; sparsity = TracerSparsityDetector()) | ||
| nlfunc_dense = NLS.NonlinearFunction(form_residual!) | ||
| nlfunc_sparse = NLS.NonlinearFunction(form_residual!; sparsity = SparseConnectivityTracer.TracerSparsityDetector()) |
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| nlfunc_sparse = NLS.NonlinearFunction(form_residual!; sparsity = SparseConnectivityTracer.TracerSparsityDetector()) | |
| nlfunc_sparse = NLS.NonlinearFunction( | |
| form_residual!; sparsity = SparseConnectivityTracer.TracerSparsityDetector()) |
| prob_brusselator_2d_autosparse = NonlinearProblem( | ||
| NonlinearFunction(brusselator_2d_loop; sparsity = TracerSparsityDetector()), | ||
| prob_brusselator_2d_autosparse = NLS.NonlinearProblem( | ||
| NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), |
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| NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), | |
| NLS.NonlinearFunction( | |
| brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), |
| f! = (du, u) -> brusselator_2d_loop(du, u, p) | ||
| du0 = similar(u0) | ||
| jac_sparsity = ADTypes.jacobian_sparsity(f!, du0, u0, TracerSparsityDetector()) | ||
| jac_sparsity = ADTypes.jacobian_sparsity(f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector()) |
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| jac_sparsity = ADTypes.jacobian_sparsity(f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector()) | |
| jac_sparsity = ADTypes.jacobian_sparsity( | |
| f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector()) |
| @btime solve(prob_brusselator_2d_sparse, NewtonRaphson(linsolve = KLUFactorization())); | ||
| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson()); | ||
| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson()); | ||
| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization())); |
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| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization())); | |
| BenchmarkTools.@btime NLS.solve( | |
| prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization())); |
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| ```@example ill_conditioned_nlprob | ||
| @btime solve(prob_brusselator_2d, NewtonRaphson(linsolve = KrylovJL_GMRES())); | ||
| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES())); |
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| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES())); | |
| BenchmarkTools.@btime NLS.solve( | |
| prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES())); |
| @btime solve(prob_brusselator_2d_sparse, | ||
| NewtonRaphson(linsolve = KrylovJL_GMRES(precs = incompletelu), concrete_jac = true) | ||
| BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, | ||
| NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true) |
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| NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true) | |
| NLS.NewtonRaphson( | |
| linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true) |
| function algebraicmultigrid(W, p = nothing) | ||
| return aspreconditioner(ruge_stuben(convert(AbstractMatrix, W))), LinearAlgebra.I | ||
| return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(AbstractMatrix, W))), LinearAlgebra.I |
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| return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(AbstractMatrix, W))), LinearAlgebra.I | |
| return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert( | |
| AbstractMatrix, W))), | |
| LinearAlgebra.I |
| prob_brusselator_2d_exact_tracer = NonlinearProblem( | ||
| NonlinearFunction(brusselator_2d_loop; sparsity = TracerSparsityDetector()), | ||
| prob_brusselator_2d_exact_tracer = NLS.NonlinearProblem( | ||
| NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), |
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| NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), | |
| NLS.NonlinearFunction( | |
| brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()), |
| sparsity = DenseSparsityDetector(AutoForwardDiff(); atol = 1e-4)), | ||
| prob_brusselator_2d_approx_di = NLS.NonlinearProblem( | ||
| NLS.NonlinearFunction(brusselator_2d_loop; | ||
| sparsity = DifferentiationInterface.DenseSparsityDetector(ADTypes.AutoForwardDiff(); atol = 1e-4)), |
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| sparsity = DifferentiationInterface.DenseSparsityDetector(ADTypes.AutoForwardDiff(); atol = 1e-4)), | |
| sparsity = DifferentiationInterface.DenseSparsityDetector( | |
| ADTypes.AutoForwardDiff(); atol = 1e-4)), |
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| NonlinearSolveSciPy.SciPyLeastSquares | ||
| NonlinearSolveSciPy.SciPyRoot | ||
| NonlinearSolveSciPy.SciPyRootScalar | ||
| ``` No newline at end of file |
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| ``` | |
| ``` |
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