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Add availability flags in NLPModelMeta and NLSMeta #525

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7e245cd
Add availability flags in NLPModelMeta and NLSMeta
amontoison Dec 1, 2025
cb2ec54
Rename the new attributes
amontoison Dec 3, 2025
99fc709
Update the documentation of some functions
amontoison Dec 4, 2025
4d72d7b
Add hprod_residual_available in NLSMeta
amontoison Dec 9, 2025
50f6b76
Add more documentation for the availability flags
amontoison Dec 9, 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
`grad_available` | `Bool` | true if the gradient of the objective is available
`jac_available` | `Bool` | true if the sparse Jacobian of the constraints is available
`hess_available` | `Bool` | true if the sparse Hessian of the Lagrangian is available
`jprod_available` | `Bool` | true if the Jacobian-vector product `J * v` is available
`jtprod_available` | `Bool` | true if the transpose Jacobian-vector product `J' * v` is available
`hprod_available` | `Bool` | true if the Hessian-vector product of the Lagrangian `H * v` is available

# Bug reports and discussions

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5 changes: 5 additions & 0 deletions docs/src/api.md
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Expand Up @@ -45,6 +45,9 @@ NLPModels instances.
| ``J(x)`` | [`jac_lin`](@ref), [`jac_nln`](@ref), [`jac`](@ref), [`jac_lin_op`](@ref), [`jac_lin_op!`](@ref), [`jac_nln_op`](@ref), [`jac_nln_op!`](@ref),[`jac_op`](@ref), [`jac_op!`](@ref), [`jac_lin_coord`](@ref), [`jac_lin_coord!`](@ref), [`jac_nln_coord`](@ref), [`jac_nln_coord!`](@ref), [`jac_coord`](@ref), [`jac_coord!`](@ref), [`jac_lin_structure`](@ref), [`jac_lin_structure!`](@ref), [`jac_nln_structure`](@ref), [`jac_nln_structure!`](@ref), [`jac_structure`](@ref), [`jprod_lin`](@ref), [`jprod_lin!`](@ref), [`jprod_nln`](@ref), [`jprod_nln!`](@ref), [`jprod`](@ref), [`jprod!`](@ref), [`jtprod_lin`](@ref), [`jtprod_lin!`](@ref), [`jtprod_nln`](@ref), [`jtprod_nln!`](@ref), [`jtprod`](@ref), [`jtprod!`](@ref) |
| ``\nabla^2 L(x,y)`` | [`hess`](@ref), [`hess_op`](@ref), [`hess_coord`](@ref), [`hess_coord!`](@ref), [`hess_structure`](@ref), [`hess_structure!`](@ref), [`hprod`](@ref), [`hprod!`](@ref), [`jth_hprod`](@ref), [`jth_hprod!`](@ref), [`jth_hess`](@ref), [`jth_hess_coord`](@ref), [`jth_hess_coord!`](@ref), [`ghjvprod`](@ref), [`ghjvprod!`](@ref) |

If only a subset of the functions listed above is implemented, you can indicate which ones are not available when creating the [`NLPModelMeta`](@ref), using the keyword arguments
`grad_available`, `jac_available`, `hess_available`, `jprod_available`, `jtprod_available`, and `hprod_available`.

## [API for NLSModels](@id nls-api)

For the Nonlinear Least Squares models, ``f(x) = \tfrac{1}{2} \Vert F(x)\Vert^2``,
Expand All @@ -59,3 +62,5 @@ and its derivatives. Namely,
| ``F(x)`` | [`residual`](@ref), [`residual!`](@ref) |
| ``J_F(x)`` | [`jac_residual`](@ref), [`jac_coord_residual`](@ref), [`jac_coord_residual!`](@ref), [`jac_structure_residual`](@ref), [`jac_structure_residual!`](@ref), [`jprod_residual`](@ref), [`jprod_residual!`](@ref), [`jtprod_residual`](@ref), [`jtprod_residual!`](@ref), [`jac_op_residual`](@ref), [`jac_op_residual!`](@ref) |
| ``\nabla^2 F_i(x)`` | [`hess_residual`](@ref), [`hess_coord_residual`](@ref), [`hess_coord_residual!`](@ref), [`hess_structure_residual`](@ref), [`hess_structure_residual!`](@ref), [`jth_hess_residual`](@ref), [`jth_hess_residual_coord`](@ref), [`jth_hess_residual_coord!`](@ref), [`hprod_residual`](@ref), [`hprod_residual!`](@ref), [`hess_op_residual`](@ref), [`hess_op_residual!`](@ref) |

If only a subset of the functions listed above is implemented, you can indicate which ones are not available when creating the [`NLSMeta`](@ref), using the keyword arguments `jac_residual_available`, `hess_residual_available`, `jprod_residual_available`, `jtprod_residual_available`, and `hprod_residual_available`.
9 changes: 9 additions & 0 deletions docs/src/guidelines.md
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Expand Up @@ -82,6 +82,15 @@ The indices of linear and nonlinear constraints are respectively available in `n
If your model uses only linear (resp. nonlinear) constraints, then it suffices to implement the `*_lin` (resp. `*_nln`) functions.
Alternatively, one could implement only the functions without the suffixes `_nln!` (e.g., only `cons!`), but this might run into errors with tools differentiating linear and nonlinear constraints.

## [Availability of the API](@id availability-api)

If only a subset of the functions listed above is implemented, you can indicate which ones are not available when creating the [`NLPModelMeta`](@ref), using the keyword arguments
`grad_available`, `jac_available`, `hess_available`, `jprod_available`, `jtprod_available`, and `hprod_available`.

By default, `grad_available`, `hess_available`, and `hprod_available` are set to `true`.
For constrained problems (`ncon > 0`), the fields `jac_available`, `jprod_available`, and `jtprod_available` are also set to `true`.
For unconstrained problems (`ncon == 0`), they default to `false`.

## [Expected behaviour](@id expected-behaviour)

The following is a non-exhaustive list of expected behaviour for methods.
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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
`grad_available` | `Bool` | true if the gradient of the objective is available
`jac_available` | `Bool` | true if the sparse Jacobian of the constraints is available
`hess_available` | `Bool` | true if the sparse Hessian of the Lagrangian is available
`jprod_available` | `Bool` | true if the Jacobian-vector product `J * v` is available
`jtprod_available` | `Bool` | true if the transpose Jacobian-vector product `J' * v` is available
`hprod_available` | `Bool` | true if the Hessian-vector product of the Lagrangian `H * v` is available

## License

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