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‎.github/workflows/breakage.yml‎

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fi
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done >> summary.md
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- name: Display summary in CI logs
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run: |
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echo "### Breakage Summary" >> $GITHUB_STEP_SUMMARY
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cat breakage/summary.md >> $GITHUB_STEP_SUMMARY
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- name: PR comment with file
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uses: actions/github-script@v6
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if: github.event.pull_request.head.repo.fork == false
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uses: actions/github-script@main
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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script: |

‎LICENSE‎

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‎LICENSE.md‎

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‎Project.toml‎

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name = "NLPModels"
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uuid = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
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version = "0.21.5"
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version = "0.21.6"
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[deps]
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FastClosures = "9aa1b823-49e4-5ca5-8b0f-3971ec8bab6a"
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[compat]
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FastClosures = "0.3.0"
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LinearAlgebra = "1.10"
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LinearOperators = "2"
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julia = "1.6.0"
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Printf = "1.10"
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SparseArrays = "1.10"
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Test = "1.10"
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julia = "1.10"
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[extras]
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

‎README.md‎

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@@ -84,40 +84,46 @@ The complete list of methods that an interface may implement can be found in the
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`NLPModelMeta` objects have the following attributes (with `S <: AbstractVector`):
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Attribute | Type | Notes
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------------|--------------------|------------------------------------
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`nvar` | `Int ` | number of variables
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`x0 ` | `S` | initial guess
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`lvar` | `S` | vector of lower bounds
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`uvar` | `S` | vector of upper bounds
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`ifix` | `Vector{Int}` | indices of fixed variables
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`ilow` | `Vector{Int}` | indices of variables with lower bound only
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`iupp` | `Vector{Int}` | indices of variables with upper bound only
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`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
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`ifree` | `Vector{Int}` | indices of free variables
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`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
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`ncon` | `Int ` | total number of general constraints
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`nlin ` | `Int ` | number of linear constraints
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`nnln` | `Int ` | number of nonlinear general constraints
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`y0 ` | `S` | initial Lagrange multipliers
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`lcon` | `S` | vector of constraint lower bounds
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`ucon` | `S` | vector of constraint upper bounds
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`lin ` | `Vector{Int}` | indices of linear constraints
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`nln` | `Vector{Int}` | indices of nonlinear constraints
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`jfix` | `Vector{Int}` | indices of equality constraints
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`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
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`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
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`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
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`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
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`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
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`nnzo` | `Int ` | number of nonzeros in the gradient
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`nnzh` | `Int ` | number of nonzeros in the sparse Hessian
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`nnzj` | `Int ` | number of nonzeros in the sparse Jacobian
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`lin_nnzj` | `Int ` | number of nonzeros in the linear part of sparse Jacobian
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`nln_nnzj` | `Int ` | number of nonzeros in the nonlinear part of sparse Jacobian
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`minimize` | `Bool ` | true if `optimize == minimize`
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`islp` | `Bool ` | true if the problem is a linear program
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`name` | `String` | problem name
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Attribute | Type | Notes
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-------------------|---------------|------------------------------------
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`nvar` | `Int` | number of variables
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`x0 ` | `S` | initial guess
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`lvar` | `S` | vector of lower bounds
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`uvar` | `S` | vector of upper bounds
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`ifix` | `Vector{Int}` | indices of fixed variables
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`ilow` | `Vector{Int}` | indices of variables with lower bound only
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`iupp` | `Vector{Int}` | indices of variables with upper bound only
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`irng` | `Vector{Int}` | indices of variables with lower and upper bound (range)
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`ifree` | `Vector{Int}` | indices of free variables
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`iinf` | `Vector{Int}` | indices of visibly infeasible bounds
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`ncon` | `Int` | total number of general constraints
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`nlin ` | `Int` | number of linear constraints
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`nnln` | `Int` | number of nonlinear general constraints
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`y0 ` | `S` | initial Lagrange multipliers
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`lcon` | `S` | vector of constraint lower bounds
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`ucon` | `S` | vector of constraint upper bounds
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`lin ` | `Vector{Int}` | indices of linear constraints
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`nln` | `Vector{Int}` | indices of nonlinear constraints
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`jfix` | `Vector{Int}` | indices of equality constraints
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`jlow` | `Vector{Int}` | indices of constraints of the form c(x) ≥ cl
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`jupp` | `Vector{Int}` | indices of constraints of the form c(x) ≤ cu
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`jrng` | `Vector{Int}` | indices of constraints of the form cl ≤ c(x) ≤ cu
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`jfree` | `Vector{Int}` | indices of "free" constraints (there shouldn't be any)
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`jinf` | `Vector{Int}` | indices of the visibly infeasible constraints
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`nnzo` | `Int` | number of nonzeros in the gradient
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`nnzj` | `Int` | number of nonzeros in the sparse Jacobian
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`lin_nnzj` | `Int` | number of nonzeros in the sparse linear constraints Jacobian
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`nln_nnzj` | `Int` | number of nonzeros in the sparse nonlinear constraints Jacobian
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`nnzh` | `Int` | number of nonzeros in the lower triangular part of the sparse Hessian of the Lagrangian
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`minimize` | `Bool` | true if `optimize == minimize`
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`islp` | `Bool` | true if the problem is a linear program
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`name` | `String` | problem name
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`grad_available` | `Bool` | true if the gradient of the objective is available
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`jac_available` | `Bool` | true if the sparse Jacobian of the constraints is available
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`hess_available` | `Bool` | true if the sparse Hessian of the Lagrangian is available
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`jprod_available` | `Bool` | true if the Jacobian-vector product `J * v` is available
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`jtprod_available` | `Bool` | true if the transpose Jacobian-vector product `J' * v` is available
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`hprod_available` | `Bool` | true if the Hessian-vector product of the Lagrangian `H * v` is available
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# Bug reports and discussions
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‎docs/Project.toml‎

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[deps]
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ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
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[compat]
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ADNLPModels = "0.8"
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Documenter = "1.0"

‎docs/src/api.md‎

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| ``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) |
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| ``\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) |
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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
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`grad_available`, `jac_available`, `hess_available`, `jprod_available`, `jtprod_available`, and `hprod_available`.
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## [API for NLSModels](@id nls-api)
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For the Nonlinear Least Squares models, ``f(x) = \tfrac{1}{2} \Vert F(x)\Vert^2``,
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| ``F(x)`` | [`residual`](@ref), [`residual!`](@ref) |
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| ``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) |
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| ``\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) |
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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`.

‎docs/src/guidelines.md‎

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- `cons_nln!(nlp, x, c)`
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- `jac_lin_structure!(nlp, jrows, jcols)`
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- `jac_nln_structure!(nlp, jrows, jcols)`
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- `jac_lin_coord!(nlp, jvals)`
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- `jac_lin_coord!(nlp, x, jvals)`
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- `jac_nln_coord!(nlp, x, jvals)`
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- `jprod_lin!(nlp, v, Jv)`
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- `jprod_lin!(nlp, x, v, Jv)`
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- `jprod_nln!(nlp, x, v, Jv)`
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- `jtprod_lin!(nlp, v, Jtv)`
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- `jtprod_lin!(nlp, x, v, Jtv)`
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- `jtprod_nln!(nlp, x, v, Jtv)`
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- `hess_coord!(nlp, x, y, hvals; obj_weight=1)`
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- `hprod!(nlp, x, y, v, Hv; obj_weight=1)`
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If your model uses only linear (resp. nonlinear) constraints, then it suffices to implement the `*_lin` (resp. `*_nln`) functions.
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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.
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## [Availability of the API](@id availability-api)
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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
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`grad_available`, `jac_available`, `hess_available`, `jprod_available`, `jtprod_available`, and `hprod_available`.
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By default, `grad_available`, `hess_available`, and `hprod_available` are set to `true`.
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For constrained problems (`ncon > 0`), the fields `jac_available`, `jprod_available`, and `jtprod_available` are also set to `true`.
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For unconstrained problems (`ncon == 0`), they default to `false`.
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## [Expected behaviour](@id expected-behaviour)
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The following is a non-exhaustive list of expected behaviour for methods.

‎docs/src/index.md‎

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

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number of times that function was called is stored inside the
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`NLPModel`. For instance
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```@example
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```julia
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using ADNLPModels, LinearAlgebra, NLPModels
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nlp = ADNLPModel(x -> dot(x, x), zeros(2))
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for i = 1:100
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To get the sum of all counters except `cons`, `jac`, `jprod` and `jtprod` called for a problem, use
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[`sum_counters`](@ref).
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```@example
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```julia
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using ADNLPModels, LinearAlgebra, NLPModels
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nlp = ADNLPModel(x -> dot(x, x), zeros(2))
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obj(nlp, rand(2))

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