# Tools

## Functions evaluations

After calling one the API functions to get a function value, the number of times that function was called is stored inside the `NLPModel`

. For instance

```
using ADNLPModels, LinearAlgebra, NLPModels
nlp = ADNLPModel(x -> dot(x, x), zeros(2))
for i = 1:100
obj(nlp, rand(2))
end
neval_obj(nlp)
```

`100`

Some counters are available for all models, some are specific. In particular, there are additional specific counters for the nonlinear least squares models.

Counter | Description |
---|---|

`neval_obj` | Objective |

`neval_grad` | Gradient |

`neval_cons` | Constraints |

`neval_cons_lin` | Linear constraints |

`neval_cons_nln` | Nonlinear constraints |

`neval_jcon` | One constraint - unused |

`neval_jgrad` | Gradient of one constraints - unused |

`neval_jac` | Jacobian |

`neval_jac_lin` | Linear constraints Jacobian |

`neval_jac_nln` | Nonlinear constraints Jacobian |

`neval_jprod` | Product of Jacobian and vector |

`neval_jprod_lin` | Product of linear constraints Jacobian and vector |

`neval_jprod_nln` | Product of nonlinear constraints Jacobian and vector |

`neval_jtprod` | Product of transposed Jacobian and vector |

`neval_jtprod_lin` | Product of transposed linear constraints Jacobian and vector |

`neval_jtprod_nln` | Product of transposed nonlinear constraints Jacobian and vector |

`neval_hess` | Hessian |

`neval_hprod` | Product of Hessian and vector |

`neval_jhess` | Individual Lagrangian Hessian evaluations |

`neval_jhprod` | Product of Hessian of j-th function and vector |

`neval_residual` | Residual function of nonlinear least squares model |

`neval_jac_residual` | Jacobian of the residual |

`neval_jprod_residual` | Product of Jacobian of residual and vector |

`neval_jtprod_residual` | Product of transposed Jacobian of residual and vector |

`neval_hess_residual` | Sum of Hessians of residuals |

`neval_jhess_residual` | Hessian of a residual component |

`neval_hprod_residual` | Product of Hessian of a residual component and vector |

To get the sum of all counters except `cons`

, `jac`

, `jprod`

and `jtprod`

called for a problem, use `sum_counters`

.

```
using ADNLPModels, LinearAlgebra, NLPModels
nlp = ADNLPModel(x -> dot(x, x), zeros(2))
obj(nlp, rand(2))
grad(nlp, rand(2))
sum_counters(nlp)
```

`2`

## Querying problem type

There are some variable for querying the problem type:

`has_bounds`

: True when not all variables are free.`bound_constrained`

: True for problems with bounded variables and no other constraints.`equality_constrained`

: True when problem is constrained only by equalities.`has_equalities`

: True when problem has at least one equality constraint.`inequality_constrained`

: True when problem is constrained by inequalities.`has_inequalities`

: True when problem has at least one inequality constraint that isn't a bound.`linearly_constrained`

: True when problem is constrained by equalities or inequalities known to be linear.`unconstrained`

: True when problem is not constrained.