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Adding jprod_residual! and jtprod_residual! #64

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Adding jprod_residual! and jtprod_residual! #64

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22 changes: 22 additions & 0 deletions src/BundleAdjustmentNLSFunctions.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -248,3 +248,25 @@ function NLPModels.jac_coord_residual!(
end
return vals
end

function NLPModels.jprod_residual!(nls, x, v, Jv)
@lencheck nls.meta.nvar x v
@lencheck nls.nls_meta.nequ Jv
increment!(nls, :neval_jprod_residual)
rows, cols = jac_structure_residual(nls)
vals = jac_coord_residual(nls, x)
decrement!(nls, :neval_jac_residual)
coo_prod!(rows, cols, vals, v, Jv)
return Jv
end

function NLPModels.jtprod_residual!(nls, x, v, Jtv)
@lencheck nls.meta.nvar x Jtv
@lencheck nls.nls_meta.nequ v
increment!(nls, :neval_jtprod_residual)
rows, cols = jac_structure_residual(nls)
vals = jac_coord_residual(nls, x)
decrement!(nls, :neval_jac_residual)
coo_prod!(cols, rows, vals, v, Jtv)
return Jtv
end
75 changes: 75 additions & 0 deletions test/testBundleAdjustmentModels.jl
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Expand Up @@ -117,6 +117,81 @@ end
@test 2.39615629098822921515e+07 ≈ norm(Jx' * Fx)
end

@testset "test jprod_residual and jtprod_residual" begin
df = problems_df()
filter_df = df[(df.name .== "problem-16-22106-pre"), :]
name = filter_df[1, :name]
model = BundleAdjustmentModel(name)
Fx = residual(model, model.meta.x0)
S = typeof(model.meta.x0)
meta_nls = nls_meta(model)
rows = Vector{Int}(undef, meta_nls.nnzj)
cols = Vector{Int}(undef, meta_nls.nnzj)
vals = S(undef, meta_nls.nnzj)
Jv = S(undef, meta_nls.nequ)
Jtv = S(undef, meta_nls.nvar)
jac_structure_residual!(model, rows, cols)
jac_coord_residual!(model, model.meta.x0, vals)
Jx = jac_op_residual!(model, rows, cols, vals, Jv, Jtv)
Jtu = S(undef, meta_nls.nvar)
jtprod_residual!(model, model.meta.x0, Fx, Jtu)

@test 1.70677551536496222019e+08 ≈ norm(Jtu)

Ju = S(undef, meta_nls.nequ)
jprod_residual!(model, model.meta.x0, model.meta.x0, Ju)

@test norm(Jx * model.meta.x0) ≈ norm(Ju)

filter_df = df[(df.name .== "problem-21-11315-pre"), :]
name = filter_df[1, :name]
model = BundleAdjustmentModel(name)
Fx = residual(model, model.meta.x0)
S = typeof(model.meta.x0)
meta_nls = nls_meta(model)
rows = Vector{Int}(undef, meta_nls.nnzj)
cols = Vector{Int}(undef, meta_nls.nnzj)
vals = S(undef, meta_nls.nnzj)
Jv = S(undef, meta_nls.nequ)
Jtv = S(undef, meta_nls.nvar)
jac_structure_residual!(model, rows, cols)
jac_coord_residual!(model, model.meta.x0, vals)
Jx = jac_op_residual!(model, rows, cols, vals, Jv, Jtv)
Jtu = S(undef, meta_nls.nvar)
jtprod_residual!(model, model.meta.x0, Fx, Jtu)

@test 1.64335338754470020533e+08 ≈ norm(Jtu)

Ju = S(undef, meta_nls.nequ)
jprod_residual!(model, model.meta.x0, model.meta.x0, Ju)

@test norm(Jx * model.meta.x0) ≈ norm(Ju)

filter_df = df[(df.name .== "problem-49-7776-pre"), :]
name = filter_df[1, :name]
model = BundleAdjustmentModel(name)
Fx = residual(model, model.meta.x0)
S = typeof(model.meta.x0)
meta_nls = nls_meta(model)
rows = Vector{Int}(undef, meta_nls.nnzj)
cols = Vector{Int}(undef, meta_nls.nnzj)
vals = S(undef, meta_nls.nnzj)
Jv = S(undef, meta_nls.nequ)
Jtv = S(undef, meta_nls.nvar)
jac_structure_residual!(model, rows, cols)
jac_coord_residual!(model, model.meta.x0, vals)
Jx = jac_op_residual!(model, rows, cols, vals, Jv, Jtv)
Jtu = S(undef, meta_nls.nvar)
jtprod_residual!(model, model.meta.x0, Fx, Jtu)

@test 2.39615629098822921515e+07 ≈ norm(Jtu)

Ju = S(undef, meta_nls.nequ)
jprod_residual!(model, model.meta.x0, model.meta.x0, Ju)

@test norm(Jx * model.meta.x0) ≈ norm(Ju)
end

@testset "test delete_ba_artifact!()" begin
df = problems_df()
sort!(df, [:nequ, :nvar])
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