# Nonlinear programming

## Types

`MathOptInterface.AbstractNLPEvaluator`

— Type`AbstractNLPEvaluator`

Abstract supertype for the callback object that is used to query function values, derivatives, and expression graphs.

It is used in `NLPBlockData`

.

`MathOptInterface.NLPBoundsPair`

— Type`NLPBoundsPair(lower::Float64, upper::Float64)`

A struct holding a pair of lower and upper bounds.

`-Inf`

and `Inf`

can be used to indicate no lower or upper bound, respectively.

`MathOptInterface.NLPBlockData`

— Type```
struct NLPBlockData
constraint_bounds::Vector{NLPBoundsPair}
evaluator::AbstractNLPEvaluator
has_objective::Bool
end
```

A struct encoding a set of nonlinear constraints of the form $lb \le g(x) \le ub$ and, if `has_objective == true`

, a nonlinear objective function $f(x)$.

Nonlinear objectives *override* any objective set by using the `ObjectiveFunction`

attribute.

The `evaluator`

is a callback object that is used to query function values, derivatives, and expression graphs. If `has_objective == false`

, then it is an error to query properties of the objective function, and in Hessian-of-the-Lagrangian queries, `σ`

must be set to zero.

Throughout the evaluator, all variables are ordered according to `ListOfVariableIndices`

. Hence, MOI copies of nonlinear problems must not re-order variables.

## Attributes

`MathOptInterface.NLPBlock`

— Type`NLPBlock()`

An `AbstractModelAttribute`

that stores an `NLPBlockData`

, representing a set of nonlinear constraints, and optionally a nonlinear objective.

`MathOptInterface.NLPBlockDual`

— Type`NLPBlockDual(result_index::Int = 1)`

An `AbstractModelAttribute`

for the Lagrange multipliers on the constraints from the `NLPBlock`

in result `result_index`

.

If `result_index`

is omitted, it is `1`

by default.

`MathOptInterface.NLPBlockDualStart`

— Type`NLPBlockDualStart()`

An `AbstractModelAttribute`

for the initial assignment of the Lagrange multipliers on the constraints from the `NLPBlock`

that the solver may use to warm-start the solve.

## Functions

`MathOptInterface.initialize`

— Function```
initialize(
d::AbstractNLPEvaluator,
requested_features::Vector{Symbol},
)::Nothing
```

Initialize `d`

with the set of features in `requested_features`

. Check `features_available`

before calling `initialize`

to see what features are supported by `d`

.

This method must be called before any other methods.

**Features**

The following features are defined:

`:Grad`

: enables`eval_objective_gradient`

`:Jac`

: enables`eval_constraint_jacobian`

`:JacVec`

: enables`eval_constraint_jacobian_product`

and`eval_constraint_jacobian_transpose_product`

`:Hess`

: enables`eval_hessian_lagrangian`

`:HessVec`

: enables`eval_hessian_lagrangian_product`

`:ExprGraph`

: enables`objective_expr`

and`constraint_expr`

.

In all cases, including when `requested_features`

is empty, `eval_objective`

and `eval_constraint`

are supported.

**Examples**

```
MOI.initialize(d, Symbol[])
MOI.initialize(d, [:ExprGraph])
MOI.initialize(d, MOI.features_available(d))
```

`MathOptInterface.features_available`

— Function`features_available(d::AbstractNLPEvaluator)::Vector{Symbol}`

Returns the subset of features available for this problem instance.

See `initialize`

for the list of defined features.

`MathOptInterface.eval_objective`

— Function`eval_objective(d::AbstractNLPEvaluator, x::AbstractVector{T})::T where {T}`

Evaluate the objective $f(x)$, returning a scalar value.

`MathOptInterface.eval_constraint`

— Function```
eval_constraint(d::AbstractNLPEvaluator,
g::AbstractVector{T},
x::AbstractVector{T},
)::Nothing where {T}
```

Given a set of vector-valued constraints $l \le g(x) \le u$, evaluate the constraint function $g(x)$, storing the result in the vector `g`

.

**Implementation notes**

When implementing this method, you must not assume that `g`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.eval_objective_gradient`

— Function```
eval_objective_gradient(
d::AbstractNLPEvaluator,
grad::AbstractVector{T},
x::AbstractVector{T},
)::Nothing where {T}
```

Evaluate the gradient of the objective function $grad = \nabla f(x)$ as a dense vector, storing the result in the vector `grad`

.

**Implementation notes**

When implementing this method, you must not assume that `grad`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.jacobian_structure`

— Function`jacobian_structure(d::AbstractNLPEvaluator)::Vector{Tuple{Int64,Int64}}`

Returns a vector of tuples, `(row, column)`

, where each indicates the position of a structurally nonzero element in the Jacobian matrix: $J_g(x) = \left[ \begin{array}{c} \nabla g_1(x) \\ \nabla g_2(x) \\ \vdots \\ \nabla g_m(x) \end{array}\right],$ where $g_i$ is the $i\text{th}$ component of the nonlinear constraints $g(x)$.

The indices are not required to be sorted and can contain duplicates, in which case the solver should combine the corresponding elements by adding them together.

The sparsity structure is assumed to be independent of the point $x$.

`MathOptInterface.hessian_lagrangian_structure`

— Function```
hessian_lagrangian_structure(
d::AbstractNLPEvaluator,
)::Vector{Tuple{Int64,Int64}}
```

Returns a vector of tuples, `(row, column)`

, where each indicates the position of a structurally nonzero element in the Hessian-of-the-Lagrangian matrix: $\nabla^2 f(x) + \sum_{i=1}^m \nabla^2 g_i(x)$.

The indices are not required to be sorted and can contain duplicates, in which case the solver should combine the corresponding elements by adding them together.

Any mix of lower and upper-triangular indices is valid. Elements `(i,j)`

and `(j,i)`

, if both present, should be treated as duplicates.

The sparsity structure is assumed to be independent of the point $x$.

`MathOptInterface.eval_constraint_jacobian`

— Function```
eval_constraint_jacobian(d::AbstractNLPEvaluator,
J::AbstractVector{T},
x::AbstractVector{T},
)::Nothing where {T}
```

Evaluates the sparse Jacobian matrix $J_g(x) = \left[ \begin{array}{c} \nabla g_1(x) \\ \nabla g_2(x) \\ \vdots \\ \nabla g_m(x) \end{array}\right]$.

The result is stored in the vector `J`

in the same order as the indices returned by `jacobian_structure`

.

**Implementation notes**

When implementing this method, you must not assume that `J`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.eval_constraint_jacobian_product`

— Function```
eval_constraint_jacobian_product(
d::AbstractNLPEvaluator,
y::AbstractVector{T},
x::AbstractVector{T},
w::AbstractVector{T},
)::Nothing where {T}
```

Computes the Jacobian-vector product $y = J_g(x)w$, storing the result in the vector `y`

.

The vectors have dimensions such that `length(w) == length(x)`

, and `length(y)`

is the number of nonlinear constraints.

**Implementation notes**

When implementing this method, you must not assume that `y`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.eval_constraint_jacobian_transpose_product`

— Function```
eval_constraint_jacobian_transpose_product(
d::AbstractNLPEvaluator,
y::AbstractVector{T},
x::AbstractVector{T},
w::AbstractVector{T},
)::Nothing where {T}
```

Computes the Jacobian-transpose-vector product $y = J_g(x)^Tw$, storing the result in the vector `y`

.

The vectors have dimensions such that `length(y) == length(x)`

, and `length(w)`

is the number of nonlinear constraints.

**Implementation notes**

When implementing this method, you must not assume that `y`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.eval_hessian_lagrangian`

— Function```
eval_hessian_lagrangian(
d::AbstractNLPEvaluator,
H::AbstractVector{T},
x::AbstractVector{T},
σ::T,
μ::AbstractVector{T},
)::Nothing where {T}
```

Given scalar weight `σ`

and vector of constraint weights `μ`

, this function computes the sparse Hessian-of-the-Lagrangian matrix: $\sigma\nabla^2 f(x) + \sum_{i=1}^m \mu_i \nabla^2 g_i(x)$, storing the result in the vector `H`

in the same order as the indices returned by `hessian_lagrangian_structure`

.

**Implementation notes**

When implementing this method, you must not assume that `H`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.eval_hessian_lagrangian_product`

— Function```
eval_hessian_lagrangian_product(
d::AbstractNLPEvaluator,
h::AbstractVector{T},
x::AbstractVector{T},
v::AbstractVector{T},
σ::T,
μ::AbstractVector{T},
)::Nothing where {T}
```

Given scalar weight `σ`

and vector of constraint weights `μ`

, computes the Hessian-of-the-Lagrangian-vector product $h = \left(\sigma\nabla^2 f(x) + \sum_{i=1}^m \mu_i \nabla^2 g_i(x)\right)v$, storing the result in the vector `h`

.

The vectors have dimensions such that `length(h) == length(x) == length(v)`

.

**Implementation notes**

When implementing this method, you must not assume that `h`

is `Vector{Float64}`

, but you may assume that it supports `setindex!`

and `length`

. For example, it may be the `view`

of a vector.

`MathOptInterface.objective_expr`

— Function`objective_expr(d::AbstractNLPEvaluator)::Expr`

Returns a Julia `Expr`

object representing the expression graph of the objective function.

**Format**

The expression has a number of limitations, compared with arbitrary Julia expressions:

- All sums and products are flattened out as simple
`Expr(:+, ...)`

and`Expr(:*, ...)`

objects. - All decision variables must be of the form
`Expr(:ref, :x, MOI.VariableIndex(i))`

, where`i`

is the $i$th variable in`ListOfVariableIndices`

. - There are currently no restrictions on recognized functions; typically these will be built-in Julia functions like
`^`

,`exp`

,`log`

,`cos`

,`tan`

,`sqrt`

, etc., but modeling interfaces may choose to extend these basic functions, or error if they encounter unsupported functions.

**Examples**

The expression $x_1+\sin(x_2/\exp(x_3))$ is represented as

`:(x[MOI.VariableIndex(1)] + sin(x[MOI.VariableIndex(2)] / exp(x[MOI.VariableIndex[3]])))`

or equivalently

```
Expr(
:call,
:+,
Expr(:ref, :x, MOI.VariableIndex(1)),
Expr(
:call,
:/,
Expr(:call, :sin, Expr(:ref, :x, MOI.VariableIndex(2))),
Expr(:call, :exp, Expr(:ref, :x, MOI.VariableIndex(3))),
),
)
```

`MathOptInterface.constraint_expr`

— Function`constraint_expr(d::AbstractNLPEvaluator, i::Integer)::Expr`

Returns a Julia `Expr`

object representing the expression graph for the $i\text{th}$ nonlinear constraint.

**Format**

The format is the same as `objective_expr`

, with an additional comparison operator indicating the sense of and bounds on the constraint.

For single-sided comparisons, the body of the constraint must be on the left-hand side, and the right-hand side must be a constant.

For double-sided comparisons (that is, $l \le f(x) \le u$), the body of the constraint must be in the middle, and the left- and right-hand sides must be constants.

The bounds on the constraints must match the `NLPBoundsPair`

s passed to `NLPBlockData`

.

**Examples**

```
:(x[MOI.VariableIndex(1)]^2 <= 1.0)
:(x[MOI.VariableIndex(1)]^2 >= 2.0)
:(x[MOI.VariableIndex(1)]^2 == 3.0)
:(4.0 <= x[MOI.VariableIndex(1)]^2 <= 5.0)
```