Update jac_lin_coord methods (#501)

* Update guidelines.md

* Update api.jl

* Update api.jl

* Update simple-model.jl

* Update api.jl

* Update api.jl

* Update dummy-model.jl

* Update api.jl

* Update api.jl

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update docs/src/guidelines.md

Co-authored-by: Tangi Migot <[email protected]>

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* fixing

* passing failing checks

* Update src/nlp/api.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update test/nlp/simple-model.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update test/nlp/simple-model.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update test/nls/simple-model.jl

Co-authored-by: Tangi Migot <[email protected]>

* Update test/nlp/dummy-model.jl

Co-authored-by: Tangi Migot <[email protected]>

---------

Co-authored-by: Tangi Migot <[email protected]>

Author: arnavk23

docs/src/guidelines.md

@@ -68,11 +68,11 @@ The following functions should be defined:
   - `cons_nln!(nlp, x, c)`
   - `jac_lin_structure!(nlp, jrows, jcols)`
   - `jac_nln_structure!(nlp, jrows, jcols)`
-  - `jac_lin_coord!(nlp, x, jvals)`
+  - `jac_lin_coord!(nlp, jvals)`
   - `jac_nln_coord!(nlp, x, jvals)`
-  - `jprod_lin!(nlp, x, v, Jv)`
+  - `jprod_lin!(nlp, v, Jv)`
   - `jprod_nln!(nlp, x, v, Jv)`
-  - `jtprod_lin!(nlp, x, v, Jtv)`
+  - `jtprod_lin!(nlp, v, Jtv)`
   - `jtprod_nln!(nlp, x, v, Jtv)`
   - `hess_coord!(nlp, x, y, hvals; obj_weight=1)`
   - `hprod!(nlp, x, y, v, Hv; obj_weight=1)`

src/nlp/api.jl

@@ -1,3 +1,5 @@
+using Base: @deprecate
+
 export obj, grad, grad!, objgrad, objgrad!, objcons, objcons!
 export cons, cons!, cons_lin, cons_lin!, cons_nln, cons_nln!
 export jth_con, jth_congrad, jth_congrad!, jth_sparse_congrad
@@ -266,10 +268,10 @@ function jac_coord!(nlp::AbstractNLPModel, x::AbstractVector, vals::AbstractVect
   increment!(nlp, :neval_jac)
   if nlp.meta.nlin > 0
     if nlp.meta.nnln == 0
-      jac_lin_coord!(nlp, x, vals)
+      jac_lin_coord!(nlp, vals)
     else
       lin_ind = 1:(nlp.meta.lin_nnzj)
-      jac_lin_coord!(nlp, x, view(vals, lin_ind))
+      jac_lin_coord!(nlp, view(vals, lin_ind))
     end
   end
   if nlp.meta.nnln > 0
@@ -307,36 +309,40 @@ function jac(nlp::AbstractNLPModel, x::AbstractVector)
 end
 
 """
-    vals = jac_lin_coord!(nlp, x, vals)
+    vals = jac_lin_coord!(nlp, vals)
 
-Evaluate ``J(x)``, the linear constraints Jacobian at `x` in sparse coordinate format,
+Evaluate the linear constraints Jacobian in sparse coordinate format,
 overwriting `vals`.
 """
 function jac_lin_coord! end
 
+@deprecate jac_lin_coord!(nlp::AbstractNLPModel, x::AbstractVector, vals::AbstractVector) jac_lin_coord!(nlp, vals)
+
 """
-    vals = jac_lin_coord(nlp, x)
+    vals = jac_lin_coord(nlp)
 
-Evaluate ``J(x)``, the linear constraints Jacobian at `x` in sparse coordinate format.
+Evaluate the linear constraints Jacobian in sparse coordinate format.
 """
-function jac_lin_coord(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
-  @lencheck nlp.meta.nvar x
+function jac_lin_coord(nlp::AbstractNLPModel{T, S}) where {T, S}
   vals = S(undef, nlp.meta.lin_nnzj)
-  return jac_lin_coord!(nlp, x, vals)
+  return jac_lin_coord!(nlp, vals)
 end
 
+@deprecate jac_lin_coord(nlp::AbstractNLPModel, x::AbstractVector) jac_lin_coord(nlp)
+
 """
-    Jx = jac_lin(nlp, x)
+    Jx = jac_lin(nlp)
 
-Evaluate ``J(x)``, the linear constraints Jacobian at `x` as a sparse matrix.
+Evaluate the linear constraints Jacobian as a sparse matrix.
 """
-function jac_lin(nlp::AbstractNLPModel, x::AbstractVector)
-  @lencheck nlp.meta.nvar x
+function jac_lin(nlp::AbstractNLPModel)
   rows, cols = jac_lin_structure(nlp)
-  vals = jac_lin_coord(nlp, x)
+  vals = jac_lin_coord(nlp)
   sparse(rows, cols, vals, nlp.meta.nlin, nlp.meta.nvar)
 end
 
+@deprecate jac_lin(nlp::AbstractNLPModel, x::AbstractVector) jac_lin(nlp)
+
 """
     vals = jac_nln_coord!(nlp, x, vals)
 
@@ -390,9 +396,9 @@ function jprod!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jv:
   increment!(nlp, :neval_jprod)
   if nlp.meta.nlin > 0
     if nlp.meta.nnln == 0
-      jprod_lin!(nlp, x, v, Jv)
+      jprod_lin!(nlp, v, Jv)
     else
-      jprod_lin!(nlp, x, v, view(Jv, nlp.meta.lin))
+      jprod_lin!(nlp, v, view(Jv, nlp.meta.lin))
     end
   end
   if nlp.meta.nnln > 0
@@ -427,23 +433,26 @@ function jprod!(
 end
 
 """
-    Jv = jprod_lin(nlp, x, v)
+    Jv = jprod_lin(nlp, v)
 
 Evaluate ``J(x)v``, the linear Jacobian-vector product at `x`.
 """
-function jprod_lin(nlp::AbstractNLPModel{T, S}, x::AbstractVector, v::AbstractVector) where {T, S}
-  @lencheck nlp.meta.nvar x v
+function jprod_lin(nlp::AbstractNLPModel{T, S}, v::AbstractVector) where {T, S}
+  @lencheck nlp.meta.nvar v
   Jv = S(undef, nlp.meta.nlin)
-  return jprod_lin!(nlp, x, v, Jv)
+  return jprod_lin!(nlp, v, Jv)
 end
 
 """
-    Jv = jprod_lin!(nlp, x, v, Jv)
+    Jv = jprod_lin!(nlp, v, Jv)
 
 Evaluate ``J(x)v``, the linear Jacobian-vector product at `x` in place.
 """
 function jprod_lin! end
 
+@deprecate jprod_lin(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector) jprod_lin(nlp, v)
+@deprecate jprod_lin!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jv::AbstractVector) jprod_lin!(nlp, v, Jv)
+
 """
     Jv = jprod_lin!(nlp, rows, cols, vals, v, Jv)
 
@@ -527,18 +536,18 @@ function jtprod!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jt
   @lencheck nlp.meta.ncon v
   increment!(nlp, :neval_jtprod)
   if nlp.meta.nnln == 0
-    (nlp.meta.nlin > 0) && jtprod_lin!(nlp, x, v, Jtv)
+    (nlp.meta.nlin > 0) && jtprod_lin!(nlp, v, Jtv)
   elseif nlp.meta.nlin == 0
     (nlp.meta.nnln > 0) && jtprod_nln!(nlp, x, v, Jtv)
   elseif nlp.meta.nlin >= nlp.meta.nnln
-    jtprod_lin!(nlp, x, view(v, nlp.meta.lin), Jtv)
+    jtprod_lin!(nlp, view(v, nlp.meta.lin), Jtv)
     if nlp.meta.nnln > 0
       Jtv .+= jtprod_nln(nlp, x, view(v, nlp.meta.nln))
     end
   else
     jtprod_nln!(nlp, x, view(v, nlp.meta.nln), Jtv)
     if nlp.meta.nlin > 0
-      Jtv .+= jtprod_lin(nlp, x, view(v, nlp.meta.lin))
+      Jtv .+= jtprod_lin(nlp, view(v, nlp.meta.lin))
     end
   end
   return Jtv
@@ -566,24 +575,26 @@ function jtprod!(
 end
 
 """
-    Jtv = jtprod_lin(nlp, x, v)
+    Jtv = jtprod_lin(nlp, v)
 
 Evaluate ``J(x)^Tv``, the linear transposed-Jacobian-vector product at `x`.
 """
-function jtprod_lin(nlp::AbstractNLPModel{T, S}, x::AbstractVector, v::AbstractVector) where {T, S}
-  @lencheck nlp.meta.nvar x
+function jtprod_lin(nlp::AbstractNLPModel{T, S}, v::AbstractVector) where {T, S}
   @lencheck nlp.meta.nlin v
   Jtv = S(undef, nlp.meta.nvar)
-  return jtprod_lin!(nlp, x, v, Jtv)
+  return jtprod_lin!(nlp, v, Jtv)
 end
 
 """
-    Jtv = jtprod_lin!(nlp, x, v, Jtv)
+    Jtv = jtprod_lin!(nlp, v, Jtv)
 
 Evaluate ``J(x)^Tv``, the linear transposed-Jacobian-vector product at `x` in place.
 """
 function jtprod_lin! end
 
+@deprecate jtprod_lin(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector) jtprod_lin(nlp, v)
+@deprecate jtprod_lin!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jtv::AbstractVector) jtprod_lin!(nlp, v, Jtv)
+
 """
     Jtv = jtprod_lin!(nlp, rows, cols, vals, v, Jtv)
 
@@ -736,37 +747,37 @@ function jac_op!(
 end
 
 """
-    J = jac_lin_op(nlp, x)
+    J = jac_lin_op(nlp)
 
-Return the linear Jacobian at `x` as a linear operator.
+Return the linear Jacobian as a linear operator.
 The resulting object may be used as if it were a matrix, e.g., `J * v` or
 `J' * v`.
 """
-function jac_lin_op(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
-  @lencheck nlp.meta.nvar x
+function jac_lin_op(nlp::AbstractNLPModel{T, S}) where {T, S}
   Jv = S(undef, nlp.meta.nlin)
   Jtv = S(undef, nlp.meta.nvar)
-  return jac_lin_op!(nlp, x, Jv, Jtv)
+  return jac_lin_op!(nlp, Jv, Jtv)
 end
 
+@deprecate jac_lin_op(nlp::AbstractNLPModel, x::AbstractVector) jac_lin_op(nlp)
+
 """
-    J = jac_lin_op!(nlp, x, Jv, Jtv)
+    J = jac_lin_op!(nlp, Jv, Jtv)
 
-Return the linear Jacobian at `x` as a linear operator.
+Return the linear Jacobian as a linear operator.
 The resulting object may be used as if it were a matrix, e.g., `J * v` or
 `J' * v`. The values `Jv` and `Jtv` are used as preallocated storage for the
 operations.
 """
 function jac_lin_op!(
   nlp::AbstractNLPModel{T, S},
-  x::AbstractVector{T},
   Jv::AbstractVector,
   Jtv::AbstractVector,
 ) where {T, S}
-  @lencheck nlp.meta.nvar x Jtv
   @lencheck nlp.meta.nlin Jv
+  @lencheck nlp.meta.nvar Jtv
   prod! = @closure (res, v, α, β) -> begin # res = α * J * v + β * res
-    jprod_lin!(nlp, x, v, Jv)
+    jprod_lin!(nlp, v, Jv)
     if β == 0
       res .= α .* Jv
     else
@@ -775,7 +786,7 @@ function jac_lin_op!(
     return res
   end
   ctprod! = @closure (res, v, α, β) -> begin
-    jtprod_lin!(nlp, x, v, Jtv)
+    jtprod_lin!(nlp, v, Jtv)
     if β == 0
       res .= α .* Jtv
     else
@@ -786,6 +797,8 @@ function jac_lin_op!(
   return LinearOperator{T}(nlp.meta.nlin, nlp.meta.nvar, false, false, prod!, ctprod!, ctprod!)
 end
 
+@deprecate jac_lin_op!(nlp::AbstractNLPModel, x::AbstractVector, Jv::AbstractVector, Jtv::AbstractVector) jac_lin_op!(nlp, Jv, Jtv)
+
 """
     J = jac_lin_op!(nlp, rows, cols, vals, Jv, Jtv)
 

test/nlp/api.jl

@@ -30,13 +30,13 @@
   @test cons_lin(nlp, x) == c(x)[1:1]
   @test jac(nlp, x) ≈ J(x)
   @test jac_nln(nlp, x) ≈ J(x)[2:2, :]
-  @test jac_lin(nlp, x) ≈ J(x)[1:1, :]
+  @test jac_lin(nlp) ≈ J(x)[1:1, :]
   @test jprod(nlp, x, v) ≈ J(x) * v
   @test jprod_nln(nlp, x, v) ≈ J(x)[2:2, :] * v
-  @test jprod_lin(nlp, x, v) ≈ J(x)[1:1, :] * v
+  @test jprod_lin(nlp, v) ≈ J(x)[1:1, :] * v
   @test jtprod(nlp, x, w) ≈ J(x)' * w
   @test jtprod_nln(nlp, x, w[2:2]) ≈ J(x)[2:2, :]' * w[2:2]
-  @test jtprod_lin(nlp, x, w[1:1]) ≈ J(x)[1:1, :]' * w[1:1]
+  @test jtprod_lin(nlp, w[1:1]) ≈ J(x)[1:1, :]' * w[1:1]
   @test hess(nlp, x, y) ≈ tril(H(x, y))
   @test hprod(nlp, x, y, v) ≈ H(x, y) * v
 
@@ -56,12 +56,12 @@
   @test jprod!(nlp, jac_structure(nlp)..., jac_coord(nlp, x), v, Jv) ≈ J(x) * v
   @test jprod_nln!(nlp, jac_nln_structure(nlp)..., jac_nln_coord(nlp, x), v, Jv[2:2]) ≈
         J(x)[2:2, :] * v
-  @test jprod_lin!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp, x), v, Jv[1:1]) ≈
+  @test jprod_lin!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp), v, Jv[1:1]) ≈
         J(x)[1:1, :] * v
   @test jtprod!(nlp, jac_structure(nlp)..., jac_coord(nlp, x), w, Jtw) ≈ J(x)' * w
   @test jtprod_nln!(nlp, jac_nln_structure(nlp)..., jac_nln_coord(nlp, x), w[2:2], Jtw) ≈
         J(x)[2:2, :]' * w[2:2]
-  @test jtprod_lin!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp, x), w[1:1], Jtw) ≈
+  @test jtprod_lin!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp), w[1:1], Jtw) ≈
         J(x)[1:1, :]' * w[1:1]
   Jop = jac_op!(nlp, x, Jv, Jtw)
   @test Jop * v ≈ J(x) * v
@@ -91,14 +91,14 @@
   @test mul!(w[2:2], Jop, v, 1.0, -1.0) ≈ res
   res = J(x)[2:2, :]' * w[2:2] - v
   @test mul!(v, Jop', w[2:2], 1.0, -1.0) ≈ res
-  Jop = jac_lin_op!(nlp, x, Jv[1:1], Jtw)
+  Jop = jac_lin_op!(nlp, Jv[1:1], Jtw)
   @test Jop * v ≈ J(x)[1:1, :] * v
   @test Jop' * w[1:1] ≈ Jtw
   res = J(x)[1:1, :] * v - w[1:1]
   @test mul!(w[1:1], Jop, v, 1.0, -1.0) ≈ res
   res = J(x)[1:1, :]' * w[1:1] - v
   @test mul!(v, Jop', w[1:1], 1.0, -1.0) ≈ res
-  Jop = jac_lin_op!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp, x), Jv[1:1], Jtw)
+  Jop = jac_lin_op!(nlp, jac_lin_structure(nlp)..., jac_lin_coord(nlp), Jv[1:1], Jtw)
   @test Jop * v ≈ J(x)[1:1, :] * v
   @test Jop' * w[1:1] ≈ Jtw
   res = J(x)[1:1, :] * v - w[1:1]

test/nlp/dummy-model.jl

@@ -19,15 +19,15 @@ end
   @test_throws(MethodError, jth_congrad(model, [0.0], 1))
   @test_throws(MethodError, jth_sparse_congrad(model, [0.0], 1))
   @test_throws(MethodError, jth_congrad!(model, [0.0], 1, [2.0]))
-  @test_throws(MethodError, jprod_lin!(model, [0.0], [1.0], [2.0]))
-  @test_throws(MethodError, jtprod_lin!(model, [0.0], [1.0], [2.0]))
+  @test_throws(MethodError, jprod_lin!(model, [0.0], [2.0]))
+  @test_throws(MethodError, jtprod_lin!(model, [0.0], [2.0]))
   @test_throws(MethodError, jprod_nln!(model, [0.0], [1.0], [2.0]))
   @test_throws(MethodError, jtprod_nln!(model, [0.0], [1.0], [2.0]))
   @test_throws(MethodError, jth_hess_coord!(model, [0.0], 1))
   @test_throws(MethodError, jth_hprod!(model, [0.0], [1.0], 2, [3.0]))
   @test_throws(MethodError, ghjvprod!(model, [0.0], [1.0], [2.0], [3.0]))
   @assert isa(hess_op(model, [0.0]), LinearOperator)
   @assert isa(jac_op(model, [0.0]), LinearOperator)
-  @assert isa(jac_lin_op(model, [0.0]), LinearOperator)
+  @assert isa(jac_lin_op(model), LinearOperator)
   @assert isa(jac_nln_op(model, [0.0]), LinearOperator)
 end

test/nlp/simple-model.jl

@@ -139,8 +139,8 @@ function NLPModels.jac_nln_coord!(nlp::SimpleNLPModel, x::AbstractVector, vals::
   return vals
 end
 
-function NLPModels.jac_lin_coord!(nlp::SimpleNLPModel, x::AbstractVector, vals::AbstractVector)
-  @lencheck 2 x vals
+function NLPModels.jac_lin_coord!(nlp::SimpleNLPModel, vals::AbstractVector)
+  @lencheck 2 vals
   increment!(nlp, :neval_jac_lin)
   vals .= [1, -2]
   return vals
@@ -159,16 +159,12 @@ function NLPModels.jprod_nln!(
   return Jv
 end
 
-function NLPModels.jprod_lin!(
-  nlp::SimpleNLPModel,
-  x::AbstractVector,
-  v::AbstractVector,
-  Jv::AbstractVector,
-)
-  @lencheck 2 x v
+
+function NLPModels.jprod_lin!(nlp::SimpleNLPModel, v::AbstractVector, Jv::AbstractVector)
+  @lencheck 2 v
   @lencheck 1 Jv
   increment!(nlp, :neval_jprod_lin)
-  Jv .= [v[1] - 2 * v[2]]
+  Jv[1] = v[1] - 2 * v[2]
   return Jv
 end
 
@@ -185,13 +181,8 @@ function NLPModels.jtprod_nln!(
   return Jtv
 end
 
-function NLPModels.jtprod_lin!(
-  nlp::SimpleNLPModel,
-  x::AbstractVector,
-  v::AbstractVector,
-  Jtv::AbstractVector,
-)
-  @lencheck 2 x Jtv
+function NLPModels.jtprod_lin!(nlp::SimpleNLPModel, v::AbstractVector, Jtv::AbstractVector)
+  @lencheck 2 Jtv
   @lencheck 1 v
   increment!(nlp, :neval_jtprod_lin)
   Jtv .= [v[1]; -2 * v[1]]