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Add availability flags in NLPModelMeta and NLSMeta #524

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4b6d2bb
Add availability flags in NLPModelMeta
amontoison Dec 1, 2025
734bd93
Also add a flag for the gradient
amontoison Dec 1, 2025
77714b1
Update NLSMeta
amontoison Dec 1, 2025
7d8d4d2
Fix typo
amontoison Dec 1, 2025
3b83db2
Update README.md
amontoison Dec 1, 2025
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74 changes: 40 additions & 34 deletions README.md
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Expand Up @@ -84,40 +84,46 @@ The complete list of methods that an interface may implement can be found in the

`NLPModelMeta` objects have the following attributes (with `S <: AbstractVector`):

Attribute | Type | Notes
------------|--------------------|------------------------------------
`nvar` | `Int ` | number of variables
`x0 ` | `S` | initial guess
`lvar` | `S` | vector of lower bounds
`uvar` | `S` | vector of upper bounds
`ifix` | `Vector{Int}` | indices of fixed variables
`ilow` | `Vector{Int}` | indices of variables with lower bound only
`iupp` | `Vector{Int}` | indices of variables with upper bound only
`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
`ifree` | `Vector{Int}` | indices of free variables
`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
`ncon` | `Int ` | total number of general constraints
`nlin ` | `Int ` | number of linear constraints
`nnln` | `Int ` | number of nonlinear general constraints
`y0 ` | `S` | initial Lagrange multipliers
`lcon` | `S` | vector of constraint lower bounds
`ucon` | `S` | vector of constraint upper bounds
`lin ` | `Vector{Int}` | indices of linear constraints
`nln` | `Vector{Int}` | indices of nonlinear constraints
`jfix` | `Vector{Int}` | indices of equality constraints
`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
`nnzo` | `Int ` | number of nonzeros in the gradient
`nnzh` | `Int ` | number of nonzeros in the sparse Hessian
`nnzj` | `Int ` | number of nonzeros in the sparse Jacobian
`lin_nnzj` | `Int ` | number of nonzeros in the linear part of sparse Jacobian
`nln_nnzj` | `Int ` | number of nonzeros in the nonlinear part of sparse Jacobian
`minimize` | `Bool ` | true if `optimize == minimize`
`islp` | `Bool ` | true if the problem is a linear program
`name` | `String` | problem name
Attribute | Type | Notes
---------------------|---------------|------------------------------------
`nvar` | `Int` | number of variables
`x0 ` | `S` | initial guess
`lvar` | `S` | vector of lower bounds
`uvar` | `S` | vector of upper bounds
`ifix` | `Vector{Int}` | indices of fixed variables
`ilow` | `Vector{Int}` | indices of variables with lower bound only
`iupp` | `Vector{Int}` | indices of variables with upper bound only
`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
`ifree` | `Vector{Int}` | indices of free variables
`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
`ncon` | `Int` | total number of general constraints
`nlin ` | `Int` | number of linear constraints
`nnln` | `Int` | number of nonlinear general constraints
`y0 ` | `S` | initial Lagrange multipliers
`lcon` | `S` | vector of constraint lower bounds
`ucon` | `S` | vector of constraint upper bounds
`lin ` | `Vector{Int}` | indices of linear constraints
`nln` | `Vector{Int}` | indices of nonlinear constraints
`jfix` | `Vector{Int}` | indices of equality constraints
`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
`nnzo` | `Int` | number of nonzeros in the gradient
`nnzj` | `Int` | number of nonzeros in the sparse Jacobian
`lin_nnzj` | `Int` | number of nonzeros in the sparse linear constraints Jacobian
`nln_nnzj` | `Int` | number of nonzeros in the sparse nonlinear constraints Jacobian
`nnzh` | `Int` | number of nonzeros in the lower triangular part of the sparse Hessian of the Lagrangian
`minimize` | `Bool` | true if `optimize == minimize`
`islp` | `Bool` | true if the problem is a linear program
`name` | `String` | problem name
`gradient_available` | `Bool` | true if the gradient of the objective is available
`jacobian_available` | `Bool` | true if the sparse Jacobian of the constraints is available
`hessian_available` | `Bool` | true if the sparse Hessian of the Lagrangian is available
`Jv_available` | `Bool` | true if the Jacobian-vector product `J * v` is available
`Jtv_available` | `Bool` | true if the transpose Jacobian-vector product `J' * v` is available
`Hv_available` | `Bool` | true if the Hessian-vector product of the Lagrangian `H * v` is available

# Bug reports and discussions

Expand Down
74 changes: 40 additions & 34 deletions docs/src/index.md
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Expand Up @@ -79,40 +79,46 @@ See the [Models](@ref), the [Tools](@ref tools-section), or the [API](@ref).

`NLPModelMeta` objects have the following attributes (with `S <: AbstractVector`):

Attribute | Type | Notes
------------|--------------------|------------------------------------
`nvar` | `Int ` | number of variables
`x0 ` | `S` | initial guess
`lvar` | `S` | vector of lower bounds
`uvar` | `S` | vector of upper bounds
`ifix` | `Vector{Int}` | indices of fixed variables
`ilow` | `Vector{Int}` | indices of variables with lower bound only
`iupp` | `Vector{Int}` | indices of variables with upper bound only
`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
`ifree` | `Vector{Int}` | indices of free variables
`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
`ncon` | `Int ` | total number of general constraints
`nlin ` | `Int ` | number of linear constraints
`nnln` | `Int ` | number of nonlinear general constraints
`y0 ` | `S` | initial Lagrange multipliers
`lcon` | `S` | vector of constraint lower bounds
`ucon` | `S` | vector of constraint upper bounds
`lin ` | `Vector{Int}` | indices of linear constraints
`nln` | `Vector{Int}` | indices of nonlinear constraints
`jfix` | `Vector{Int}` | indices of equality constraints
`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
`nnzo` | `Int ` | number of nonzeros in the gradient
`nnzj` | `Int ` | number of nonzeros in the sparse Jacobian
`lin_nnzj` | `Int ` | number of nonzeros in the sparse linear constraints Jacobian
`nln_nnzj` | `Int ` | number of nonzeros in the sparse nonlinear constraints Jacobian
`nnzh` | `Int ` | number of nonzeros in the lower triangular part of the sparse Hessian of the Lagrangian
`minimize` | `Bool ` | true if `optimize == minimize`
`islp` | `Bool ` | true if the problem is a linear program
`name` | `String` | problem name
Attribute | Type | Notes
---------------------|---------------|------------------------------------
`nvar` | `Int` | number of variables
`x0 ` | `S` | initial guess
`lvar` | `S` | vector of lower bounds
`uvar` | `S` | vector of upper bounds
`ifix` | `Vector{Int}` | indices of fixed variables
`ilow` | `Vector{Int}` | indices of variables with lower bound only
`iupp` | `Vector{Int}` | indices of variables with upper bound only
`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
`ifree` | `Vector{Int}` | indices of free variables
`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
`ncon` | `Int` | total number of general constraints
`nlin ` | `Int` | number of linear constraints
`nnln` | `Int` | number of nonlinear general constraints
`y0 ` | `S` | initial Lagrange multipliers
`lcon` | `S` | vector of constraint lower bounds
`ucon` | `S` | vector of constraint upper bounds
`lin ` | `Vector{Int}` | indices of linear constraints
`nln` | `Vector{Int}` | indices of nonlinear constraints
`jfix` | `Vector{Int}` | indices of equality constraints
`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
`nnzo` | `Int` | number of nonzeros in the gradient
`nnzj` | `Int` | number of nonzeros in the sparse Jacobian
`lin_nnzj` | `Int` | number of nonzeros in the sparse linear constraints Jacobian
`nln_nnzj` | `Int` | number of nonzeros in the sparse nonlinear constraints Jacobian
`nnzh` | `Int` | number of nonzeros in the lower triangular part of the sparse Hessian of the Lagrangian
`minimize` | `Bool` | true if `optimize == minimize`
`islp` | `Bool` | true if the problem is a linear program
`name` | `String` | problem name
`gradient_available` | `Bool` | true if the gradient of the objective is available
`jacobian_available` | `Bool` | true if the sparse Jacobian of the constraints is available
`hessian_available` | `Bool` | true if the sparse Hessian of the Lagrangian is available
`Jv_available` | `Bool` | true if the Jacobian-vector product `J * v` is available
`Jtv_available` | `Bool` | true if the transpose Jacobian-vector product `J' * v` is available
`Hv_available` | `Bool` | true if the Hessian-vector product of the Lagrangian `H * v` is available

## License

Expand Down
45 changes: 41 additions & 4 deletions src/nlp/meta.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,12 @@ The following keyword arguments are accepted:
- `minimize`: true if optimize == minimize
- `islp`: true if the problem is a linear program
- `name`: problem name
- `gradient_available`: indicates whether the gradient of the objective is available
- `jacobian_available`: indicates whether the sparse Jacobian of the constraints is available
- `hessian_available`: indicates whether the sparse Hessian of the Lagrangian is available
- `Jv_available`: indicates whether the Jacobian-vector product `J * v` is available
- `Jtv_available`: indicates whether the transpose Jacobian-vector product `J' * v` is available
- `Hv_available`: indicates whether the Hessian-vector product of the Lagrangian `H * v` is available

`NLPModelMeta` also contains the following attributes, which are computed from the variables above:
- `nvar`: number of variables
Expand Down Expand Up @@ -114,6 +120,13 @@ struct NLPModelMeta{T, S} <: AbstractNLPModelMeta{T, S}
minimize::Bool
islp::Bool
name::String

gradient_available::Bool
jacobian_available::Bool
hessian_available::Bool
Jv_available::Bool
Jtv_available::Bool
Hv_available::Bool
end

function NLPModelMeta{T, S}(
Expand All @@ -134,9 +147,15 @@ function NLPModelMeta{T, S}(
nln_nnzj = nnzj - lin_nnzj,
nnzh = nvar * (nvar + 1) / 2,
lin = Int[],
minimize = true,
islp = false,
minimize::Bool = true,
islp::Bool = false,
name = "Generic",
gradient_available::Bool = true,
jacobian_available::Bool = true,
hessian_available::Bool = true,
Jv_available::Bool = true,
Jtv_available::Bool = true,
Hv_available::Bool = true,
) where {T, S}
if (nvar < 1) || (ncon < 0)
error("Nonsensical dimensions")
Expand Down Expand Up @@ -213,6 +232,12 @@ function NLPModelMeta{T, S}(
minimize,
islp,
name,
gradient_available,
jacobian_available,
hessian_available,
Jv_available,
Jtv_available,
Hv_available,
)
end

Expand All @@ -238,9 +263,15 @@ function NLPModelMeta(
nln_nnzj = meta.nln_nnzj,
nnzh = meta.nnzh,
lin = meta.lin,
minimize = meta.minimize,
islp = meta.islp,
minimize::Bool = meta.minimize,
islp::Bool = meta.islp,
name = meta.name,
gradient_available::Bool = meta.gradient_available,
jacobian_available::Bool = meta.jacobian_available,
hessian_available::Bool = meta.hessian_available,
Jv_available::Bool = meta.Jv_available,
Jtv_available::Bool = meta.Jtv_available,
Hv_available::Bool = meta.Hv_available,
) where {T, S}
NLPModelMeta{T, S}(
nvar,
Expand All @@ -263,6 +294,12 @@ function NLPModelMeta(
minimize = minimize,
islp = islp,
name = name,
gradient_available = gradient_available,
jacobian_available = jacobian_available,
hessian_available = hessian_available,
Jv_available = Jv_available,
Jtv_available = Jtv_available,
Hv_available = Hv_available,
)
end

Expand Down
20 changes: 19 additions & 1 deletion src/nls/meta.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,11 @@ The following keyword arguments are accepted:
- `nnzj`: number of elements needed to store the nonzeros of the Jacobian of the residual
- `nnzh`: number of elements needed to store the nonzeros of the sum of Hessians of the residuals
- `lin`: indices of linear residuals
- `jacobian_residual_available`: indicates whether the sparse Jacobian of the residuals is available
- `hessian_residual_available`: indicates whether the sum of the sparse Hessians of the residuals is available
- `Jv_residual_available`: indicates whether the Jacobian-vector product for the residuals is available
- `Jtv_residual_available`: indicates whether the transpose Jacobian-vector product for the residuals is available
- `Hv_residual_available`: indicates whether the sum of Hessian-vector product for the residuals is available

`NLSMeta` also contains the following attributes, which are computed from the variables above:
- `nequ`: size of the residual
Expand All @@ -35,13 +40,24 @@ struct NLSMeta{T, S}
lin::Vector{Int} # List of linear residuals
nlin::Int # = length(lin)

jacobian_residual_available::Bool
hessian_residual_available::Bool
Jv_residual_available::Bool
Jtv_residual_available::Bool
Hv_residual_available::Bool

function NLSMeta{T, S}(
nequ::Int,
nvar::Int;
x0::S = fill!(S(undef, nvar), zero(T)),
nnzj = nequ * nvar,
nnzh = div(nvar * (nvar + 1), 2),
lin = Int[],
jacobian_residual_available::Bool = true,
hessian_residual_available::Bool = true,
Jv_residual_available::Bool = true,
Jtv_residual_available::Bool = true,
Hv_residual_available::Bool = true,
) where {T, S}
nnzj = max(0, nnzj)
nnzh = max(0, nnzh)
Expand All @@ -50,7 +66,9 @@ struct NLSMeta{T, S}
nlin = length(lin)
nnln = length(nln)

return new{T, S}(nequ, nvar, x0, nnzj, nnzh, nln, nnln, lin, nlin)
return new{T, S}(nequ, nvar, x0, nnzj, nnzh, nln, nnln, lin, nlin,
jacobian_residual_available, hessian_residual_available, Jv_residual_available,
Jtv_residual_available, Hv_residual_available)
end
end

Expand Down
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