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

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_jcon`	One constraint - unused
`neval_jgrad`	Gradient of one constraints - unused
`neval_jac`	Jacobian
`neval_jprod`	Product of Jacobian and vector
`neval_jtprod`	Product of transposed Jacobian and vector
`neval_hess`	Hessian
`neval_hprod`	Product of Hessian and vector
`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 called for a problem, use sum_counters.

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

Querying problem type

There are some variable for querying the problem type:

bound_constrained: True for problems with bounded variables and no other constraints.
equality_constrained: True when problem is constrained only by equalities.
has_bounds: True when not all variables are free.
inequality_constrained: True when problem is constrained by inequalities.
linearly_constrained: True when problem is constrained by equalities or inequalities known to be linear.
unconstrained: True when problem is not constrained.

Docs

NLPModels.neval_obj — Function.

NLPModels.neval_obj(nlp)

Get the number of obj evaluations.

NLPModels.neval_grad — Function.

NLPModels.neval_grad(nlp)

Get the number of grad evaluations.

NLPModels.neval_cons — Function.

NLPModels.neval_cons(nlp)

Get the number of cons evaluations.

NLPModels.neval_jcon — Function.

NLPModels.neval_jcon(nlp)

Get the number of jcon evaluations.

NLPModels.neval_jgrad — Function.

NLPModels.neval_jgrad(nlp)

Get the number of jgrad evaluations.

NLPModels.neval_jac — Function.

NLPModels.neval_jac(nlp)

Get the number of jac evaluations.

NLPModels.neval_jprod — Function.

NLPModels.neval_jprod(nlp)

Get the number of jprod evaluations.

NLPModels.neval_jtprod — Function.

NLPModels.neval_jtprod(nlp)

Get the number of jtprod evaluations.

NLPModels.neval_hess — Function.

NLPModels.neval_hess(nlp)

Get the number of hess evaluations.

NLPModels.neval_hprod — Function.

NLPModels.neval_hprod(nlp)

Get the number of hprod evaluations.

NLPModels.neval_jhprod — Function.

NLPModels.neval_jhprod(nlp)

Get the number of jhprod evaluations.

NLPModels.neval_residual — Function.

NLPModels.neval_residual(nlp)

Get the number of residual evaluations.

NLPModels.neval_jac_residual — Function.

NLPModels.neval_jac_residual(nlp)

Get the number of jac evaluations.

NLPModels.neval_jprod_residual — Function.

NLPModels.neval_jprod_residual(nlp)

Get the number of jprod evaluations.

NLPModels.neval_jtprod_residual — Function.

NLPModels.neval_jtprod_residual(nlp)

Get the number of jtprod evaluations.

NLPModels.neval_hess_residual — Function.

NLPModels.neval_hess_residual(nlp)

Get the number of hess evaluations.

NLPModels.neval_jhess_residual — Function.

NLPModels.neval_jhess_residual(nlp)

Get the number of jhess evaluations.

NLPModels.neval_hprod_residual — Function.

NLPModels.neval_hprod_residual(nlp)

Get the number of hprod evaluations.

NLPModels.sum_counters — Function.

sum_counters(counters)

Sum all counters of counters.

sum_counters(nlp)

Sum all counters of problem nlp.

NLPModels.bound_constrained — Function.

bound_constrained(nlp)
bound_constrained(meta)

Returns whether the problem has bounds on the variables and no other constraints.

NLPModels.equality_constrained — Function.

equality_constrained(nlp)
equality_constrained(meta)

Returns whether the problem's constraints are all equalities. Unconstrained problems return false.

NLPModels.has_bounds — Function.

has_bounds(nlp)
has_bounds(meta)

Returns whether the problem has bounds on the variables.

NLPModels.inequality_constrained — Function.

inequality_constrained(nlp)
inequality_constrained(meta)

Returns whether the problem's constraints are all inequalities. Unconstrained problems return true.

NLPModels.linearly_constrained — Function.

linearly_constrained(nlp)
linearly_constrained(meta)

Returns whether the problem's constraints are known to be all linear.

NLPModels.unconstrained — Function.

unconstrained(nlp)
unconstrained(meta)

Returns whether the problem in unconstrained.