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Remove matrix-product with the structure only #382

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Nov 23, 2021
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Remove matrix-product with the structure only #382

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46a817a
rmv matrix-product with the structure only
tmigot Nov 15, 2021
603fd0b
rmv matrix operators with the structure only
tmigot Nov 16, 2021
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144 changes: 0 additions & 144 deletions src/nlp/api.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -200,21 +200,6 @@ function jprod!(
coo_prod!(rows, cols, vals, v, Jv)
end

"""
Jv = jprod!(nlp, x, rows, cols, v, Jv)

Evaluate ``J(x)v``, the Jacobian-vector product at `x` in place.
`(rows, cols)` should be the Jacobian structure in triplet format.
"""
jprod!(
nlp::AbstractNLPModel,
x::AbstractVector,
::AbstractVector{<:Integer},
::AbstractVector{<:Integer},
v::AbstractVector,
Jv::AbstractVector,
) = jprod!(nlp, x, v, Jv)

"""
Jtv = jtprod(nlp, x, v, Jtv)

Expand Down Expand Up @@ -255,21 +240,6 @@ function jtprod!(
coo_prod!(cols, rows, vals, v, Jtv)
end

"""
Jtv = jtprod!(nlp, x, rows, cols, v, Jtv)

Evaluate ``J(x)^Tv``, the transposed-Jacobian-vector product at `x` in place.
`(rows, cols)` should be the Jacobian structure in triplet format.
"""
jtprod!(
nlp::AbstractNLPModel,
x::AbstractVector,
::AbstractVector{<:Integer},
::AbstractVector{<:Integer},
v::AbstractVector,
Jtv::AbstractVector,
) = jtprod!(nlp, x, v, Jtv)

"""
J = jac_op(nlp, x)

Expand Down Expand Up @@ -368,30 +338,6 @@ function jac_op!(
)
end

"""
J = jac_op!(nlp, x, rows, cols, Jv, Jtv)

Return the Jacobian at `x` as a linear operator.
The resulting object may be used as if it were a matrix, e.g., `J * v` or
`J' * v`. `(rows, cols)` should be the sparsity structure of the Jacobian.
The values `Jv` and `Jtv` are used as preallocated storage for the operations.
"""
function jac_op!(
nlp::AbstractNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
Jv::AbstractVector,
Jtv::AbstractVector,
)
@lencheck nlp.meta.nvar x Jtv
@lencheck nlp.meta.nnzj rows cols
@lencheck nlp.meta.ncon Jv
vals = jac_coord(nlp, x)
decrement!(nlp, :neval_jac)
return jac_op!(nlp, rows, cols, vals, Jv, Jtv)
end

"""
vals = jth_hess_coord(nlp, x, j)

Expand Down Expand Up @@ -663,24 +609,6 @@ function hprod!(
coo_sym_prod!(cols, rows, vals, v, Hv)
end

"""
Hv = hprod!(nlp, x, rows, cols, v, Hv; obj_weight=1.0)

Evaluate the product of the objective Hessian at `x` with the vector `v` in
place, where the objective Hessian is
$(OBJECTIVE_HESSIAN).
`(rows, cols)` should be the Hessian structure in triplet format.
"""
hprod!(
nlp::AbstractNLPModel,
x::AbstractVector,
::AbstractVector{<:Integer},
::AbstractVector{<:Integer},
v::AbstractVector,
Hv::AbstractVector;
obj_weight::Real = 1.0,
) = hprod!(nlp, x, v, Hv, obj_weight = obj_weight)

"""
Hv = hprod!(nlp, x, y, v, Hv; obj_weight=1.0)

Expand All @@ -690,25 +618,6 @@ $(LAGRANGIAN_HESSIAN).
"""
function hprod! end

"""
Hv = hprod!(nlp, x, y, rows, cols, v, Hv; obj_weight=1.0)

Evaluate the product of the Lagrangian Hessian at `(x,y)` with the vector `v` in
place, where the Lagrangian Hessian is
$(LAGRANGIAN_HESSIAN).
`(rows, cols)` should be the Hessian structure in triplet format.
"""
hprod!(
nlp::AbstractNLPModel,
x::AbstractVector,
y::AbstractVector,
::AbstractVector{<:Integer},
::AbstractVector{<:Integer},
v::AbstractVector,
Hv::AbstractVector;
obj_weight::Real = one(eltype(x)),
) = hprod!(nlp, x, y, v, Hv, obj_weight = obj_weight)

"""
H = hess_op(nlp, x; obj_weight=1.0)

Expand Down Expand Up @@ -807,31 +716,6 @@ function hess_op!(
return LinearOperator{eltype(vals)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod!, prod!, prod!)
end

"""
H = hess_op!(nlp, x, rows, cols, Hv; obj_weight=1.0)

Return the objective Hessian at `x` with objective function scaled by
`obj_weight` as a linear operator, and storing the result on `Hv`. The resulting
object may be used as if it were a matrix, e.g., `w = H * v`.
`(rows, cols)` should be the sparsity structure of the Hessian.
The vector `Hv` is used as preallocated storage for the operation. The linear operator H
represents
$(OBJECTIVE_HESSIAN).
"""
function hess_op!(
nlp::AbstractNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
Hv::AbstractVector;
obj_weight::Real = one(eltype(x)),
)
@lencheck nlp.meta.nvar x Hv
@lencheck nlp.meta.nnzh rows cols
vals = hess_coord(nlp, x, obj_weight = obj_weight)
return hess_op!(nlp, rows, cols, vals, Hv)
end

"""
H = hess_op!(nlp, x, y, Hv; obj_weight=1.0)

Expand Down Expand Up @@ -863,34 +747,6 @@ function hess_op!(
return LinearOperator{eltype(x)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod!, prod!, prod!)
end

"""
H = hess_op!(nlp, x, y, rows, cols, Hv; obj_weight=1.0)

Return the Lagrangian Hessian at `(x,y)` with objective function scaled by
`obj_weight` as a linear operator, and storing the result on `Hv`. The resulting
object may be used as if it were a matrix, e.g., `w = H * v`.
`(rows, cols)` should be the sparsity structure of the Hessian.
The vector `Hv` is used as preallocated storage for the operation. The linear operator H
represents
$(OBJECTIVE_HESSIAN).
"""
function hess_op!(
nlp::AbstractNLPModel,
x::AbstractVector,
y::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
Hv::AbstractVector;
obj_weight::Real = one(eltype(x)),
)
@lencheck nlp.meta.nvar x Hv
@lencheck nlp.meta.ncon y
@lencheck nlp.meta.nnzh rows cols
vals = hess_coord(nlp, x, y, obj_weight = obj_weight)
decrement!(nlp, :neval_hess)
return hess_op!(nlp, rows, cols, vals, Hv)
end

function varscale end
function lagscale end
function conscale end
63 changes: 0 additions & 63 deletions src/nls/api.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -115,26 +115,6 @@ function jprod_residual!(
coo_prod!(rows, cols, vals, v, Jv)
end

"""
Jv = jprod_residual!(nls, x, rows, cols, v, Jv)

Computes the product of the Jacobian of the residual at x and a vector, i.e., ``J(x)v``, storing it in `Jv`.
The structure of the Jacobian is given by `(rows, cols)`.
"""
function jprod_residual!(
nls::AbstractNLSModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jv::AbstractVector,
)
@lencheck nls.meta.nvar x v
@lencheck nls.nls_meta.nnzj rows cols
@lencheck nls.nls_meta.nequ Jv
jprod_residual!(nls, x, v, Jv)
end

"""
Jtv = jtprod_residual(nls, x, v)

Expand Down Expand Up @@ -179,26 +159,6 @@ function jtprod_residual!(
coo_prod!(cols, rows, vals, v, Jtv)
end

"""
Jtv = jtprod_residual!(nls, x, rows, cols, v, Jtv)

Computes the product of the transpose Jacobian of the residual at x and a vector, i.e., ``J(x)^Tv``, storing it in `Jv`.
The structure of the Jacobian is given by `(rows, cols)`.
"""
function jtprod_residual!(
nls::AbstractNLSModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jtv::AbstractVector,
)
@lencheck nls.meta.nvar x Jtv
@lencheck nls.nls_meta.nnzj rows cols
@lencheck nls.nls_meta.nequ v
jtprod_residual!(nls, x, v, Jtv)
end

"""
Jx = jac_op_residual(nls, x)

Expand Down Expand Up @@ -300,29 +260,6 @@ function jac_op_residual!(
)
end

"""
Jx = jac_op_residual!(nls, x, rows, cols, Jv, Jtv)

Computes ``J(x)``, the Jacobian of the residual at x, in linear operator form. The
vectors `Jv` and `Jtv` are used as preallocated storage for the operations.
The structure of the Jacobian should be given by `(rows, cols)`.
"""
function jac_op_residual!(
nls::AbstractNLSModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
Jv::AbstractVector,
Jtv::AbstractVector,
)
@lencheck nls.meta.nvar x Jtv
@lencheck nls.nls_meta.nnzj rows cols
@lencheck nls.nls_meta.nequ Jv
vals = jac_coord_residual(nls, x)
decrement!(nls, :neval_jac_residual)
return jac_op_residual!(nls, rows, cols, vals, Jv, Jtv)
end

"""
H = hess_residual(nls, x, v)

Expand Down
11 changes: 0 additions & 11 deletions test/nlp/api.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -45,9 +45,7 @@
@test fx ≈ f(x)
@test gx ≈ ∇f(x)
@test jprod!(nlp, jac_structure(nlp)..., jac_coord(nlp, x), v, Jv) ≈ J(x) * v
@test jprod!(nlp, x, jac_structure(nlp)..., v, Jv) ≈ J(x) * v
@test jtprod!(nlp, jac_structure(nlp)..., jac_coord(nlp, x), w, Jtw) ≈ J(x)' * w
@test jtprod!(nlp, x, jac_structure(nlp)..., w, Jtw) ≈ J(x)' * w
Jop = jac_op!(nlp, x, Jv, Jtw)
@test Jop * v ≈ J(x) * v
@test Jop' * w ≈ J(x)' * w
Expand All @@ -62,9 +60,6 @@
@test mul!(w, Jop, v, 1.0, -1.0) ≈ res
res = J(x)' * w - v
@test mul!(v, Jop', w, 1.0, -1.0) ≈ res
Jop = jac_op!(nlp, x, jac_structure(nlp)..., Jv, Jtw)
@test Jop * v ≈ J(x) * v
@test Jop' * w ≈ J(x)' * w
for j = 1:(nlp.meta.ncon)
eⱼ = [i == j ? 1.0 : 0.0 for i = 1:m]
@test jth_hess(nlp, x, j) == H(x, eⱼ) - H(x)
Expand All @@ -80,8 +75,6 @@
@test ghjvprod(nlp, x, gx, v) ≈ ghjv
@test hess_coord!(nlp, x, Hvals) == hess_coord!(nlp, x, y * 0, Hvals)
@test hprod!(nlp, hess_structure(nlp)..., hess_coord(nlp, x), v, Hv) ≈ H(x) * v
@test hprod!(nlp, x, hess_structure(nlp)..., v, Hv) ≈ H(x) * v
@test hprod!(nlp, x, y, hess_structure(nlp)..., v, Hv) ≈ H(x, y) * v
Hop = hess_op(nlp, x)
@test Hop * v ≈ H(x) * v
Hop = hess_op!(nlp, x, Hv)
Expand All @@ -93,8 +86,6 @@
@test Hop * v ≈ H(x) * v
res = H(x) * v - z
@test mul!(z, Hop, v, 1.0, -1.0) ≈ res
Hop = hess_op!(nlp, x, hess_structure(nlp)..., Hv)
@test Hop * v ≈ H(x) * v
Hop = hess_op(nlp, x, y)
@test Hop * v ≈ H(x, y) * v
Hop = hess_op!(nlp, x, y, Hv)
Expand All @@ -103,8 +94,6 @@
@test mul!(z, Hop, v, 1.0, -1.0) ≈ res
Hop = hess_op!(nlp, hess_structure(nlp)..., hess_coord(nlp, x, y), Hv)
@test Hop * v ≈ H(x, y) * v
Hop = hess_op!(nlp, x, y, hess_structure(nlp)..., Hv)
@test Hop * v ≈ H(x, y) * v
end

@testset "test vector types Float32" begin
Expand Down
16 changes: 0 additions & 16 deletions test/nls/api.jl
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,8 +24,6 @@
JF(x) * v
@test jtprod_residual!(nls, jac_structure_residual(nls)..., jac_coord_residual(nls, x), w, Jtw) ≈
JF(x)' * w
@test jprod_residual!(nls, x, jac_structure_residual(nls)..., v, Jv) ≈ JF(x) * v
@test jtprod_residual!(nls, x, jac_structure_residual(nls)..., w, Jtw) ≈ JF(x)' * w
Jop = jac_op_residual(nls, x)
@test Jop * v ≈ JF(x) * v
@test Jop' * w ≈ JF(x)' * w
Expand All @@ -43,9 +41,6 @@
@test mul!(w, Jop, v, 1.0, -1.0) ≈ res
res = JF(x)' * w - v
@test mul!(v, Jop', w, 1.0, -1.0) ≈ res
Jop = jac_op_residual!(nls, x, jac_structure_residual(nls)..., Jv, Jtw)
@test Jop * v ≈ JF(x) * v
@test Jop' * w ≈ JF(x)' * w
I, J, V = findnz(sparse(HF(x, w)))
@test hess_structure_residual(nls) == (I, J)
@test hess_coord_residual(nls, x, w) ≈ V
Expand Down Expand Up @@ -111,18 +106,13 @@ end
@test fx ≈ f(x)
@test gx ≈ ∇f(x)
@test jprod!(nls, jac_structure(nls)..., jac_coord(nls, x), v, Jv) ≈ J(x) * v
@test jprod!(nls, x, jac_structure(nls)..., v, Jv) ≈ J(x) * v
@test jtprod!(nls, jac_structure(nls)..., jac_coord(nls, x), w, Jtw) ≈ J(x)' * w
@test jtprod!(nls, x, jac_structure(nls)..., w, Jtw) ≈ J(x)' * w
Jop = jac_op!(nls, x, Jv, Jtw)
@test Jop * v ≈ J(x) * v
@test Jop' * w ≈ J(x)' * w
Jop = jac_op!(nls, jac_structure(nls)..., jac_coord(nls, x), Jv, Jtw)
@test Jop * v ≈ J(x) * v
@test Jop' * w ≈ J(x)' * w
Jop = jac_op!(nls, x, jac_structure(nls)..., Jv, Jtw)
@test Jop * v ≈ J(x) * v
@test Jop' * w ≈ J(x)' * w
ghjv = zeros(m)
for j = 1:m
eⱼ = [i == j ? 1.0 : 0.0 for i = 1:m]
Expand All @@ -132,24 +122,18 @@ end
@test ghjvprod(nls, x, gx, v) ≈ ghjv
@test hess_coord!(nls, x, Hvals) == hess_coord!(nls, x, y * 0, Hvals)
@test hprod!(nls, hess_structure(nls)..., hess_coord(nls, x), v, Hv) ≈ H(x) * v
@test hprod!(nls, x, hess_structure(nls)..., v, Hv) ≈ H(x) * v
@test hprod!(nls, x, y, hess_structure(nls)..., v, Hv) ≈ H(x, y) * v
Hop = hess_op(nls, x)
@test Hop * v ≈ H(x) * v
Hop = hess_op!(nls, x, Hv)
@test Hop * v ≈ H(x) * v
Hop = hess_op!(nls, hess_structure(nls)..., hess_coord(nls, x), Hv)
@test Hop * v ≈ H(x) * v
Hop = hess_op!(nls, x, hess_structure(nls)..., Hv)
@test Hop * v ≈ H(x) * v
Hop = hess_op(nls, x, y)
@test Hop * v ≈ H(x, y) * v
Hop = hess_op!(nls, x, y, Hv)
@test Hop * v ≈ H(x, y) * v
Hop = hess_op!(nls, hess_structure(nls)..., hess_coord(nls, x, y), Hv)
@test Hop * v ≈ H(x, y) * v
Hop = hess_op!(nls, x, y, hess_structure(nls)..., Hv)
@test Hop * v ≈ H(x, y) * v
end

@testset "test vector types Float32" begin
Expand Down