MathOptInterface -> MOI

docs/src/Examples/example.md

@@ -26,7 +26,7 @@ for x_i in x
 end
 ```
 
-Now, let's consider 3 `MathOptInterface.Parameter`. Two of them, `y`, `z`, will be placed in the constraints and one, `w`, in the objective function. We'll start all three of them with a value equal to `0`:
+Now, let's consider 3 `MOI.Parameter`. Two of them, `y`, `z`, will be placed in the constraints and one, `w`, in the objective function. We'll start all three of them with a value equal to `0`:
 
 ```@example moi1
 w, cw = MOI.add_constrained_variable(optimizer, MOI.Parameter(0.0))
@@ -146,12 +146,12 @@ We declare the variable `x` as in a typical `JuMP` model:
 @variable(model, x[i = 1:2] >= 0)
 ```
 
-Now, let's consider 3 `MathOptInterface.Parameter`. Two of them, `y`, `z`, will be placed in the constraints and one, `w`, in the objective function. We'll start all three of them with a value equal to `0`:
+Now, let's consider 3 `MOI.Parameter`. Two of them, `y`, `z`, will be placed in the constraints and one, `w`, in the objective function. We'll start all three of them with a value equal to `0`:
 
 ```@example jump1
-@variable(model, y in MathOptInterface.Parameter(0.0))
-@variable(model, z in MathOptInterface.Parameter(0.0))
-@variable(model, w in MathOptInterface.Parameter(0.0))
+@variable(model, y in MOI.Parameter(0.0))
+@variable(model, z in MOI.Parameter(0.0))
+@variable(model, w in MOI.Parameter(0.0))
 ```
 
 Let's add the constraints. Notice that we treat parameters the same way we treat variables when writing the model:
@@ -246,9 +246,9 @@ const POI = ParametricOptInterface
 
 model = Model(() -> ParametricOptInterface.Optimizer(HiGHS.Optimizer()))
 @variable(model, x[i = 1:3] >= 0)
-@variable(model, p1[i = 1:3] in MathOptInterface.Parameter(0.0))
-@variable(model, p2[i = 1:3] in MathOptInterface.Parameter.([1, 10, 45]))
-@variable(model, p3[i = 1:3] in MathOptInterface.Parameter.(ones(3)))
+@variable(model, p1[i = 1:3] in MOI.Parameter(0.0))
+@variable(model, p2[i = 1:3] in MOI.Parameter.([1, 10, 45]))
+@variable(model, p3[i = 1:3] in MOI.Parameter.(ones(3)))
 ```
 
 ## JuMP Example - Dealing with parametric expressions as variable bounds
@@ -267,7 +267,7 @@ model = direct_model(POI.Optimizer(HiGHS.Optimizer()))
 @constraint(model, x >= p)
 ```
 
-Since parameters are treated like variables JuMP lowers this to MathOptInterface as `x - p >= 0` which is not a variable bound but a linear constraint.This means that the current representation of this problem at the solver level is:
+Since parameters are treated like variables JuMP lowers this to MOI as `x - p >= 0` which is not a variable bound but a linear constraint.This means that the current representation of this problem at the solver level is:
 
 ```math
 \begin{align}
@@ -343,7 +343,7 @@ MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE)
 obj_func = MOI.ScalarQuadraticFunction(
     [MOI.ScalarQuadraticTerm(1.0, x, x)
     MOI.ScalarQuadraticTerm(1.0, y, y)],
-    MathOptInterface.ScalarAffineTerm{Float64}[],
+    MOI.ScalarAffineTerm{Float64}[],
     0.0,
 )
 MOI.set(

docs/src/manual.md

@@ -56,23 +56,23 @@ optimizer = POI.Optimizer(HiGHS.Optimizer())
 
 ### Parameters
 
-A `MathOptInterface.Parameter` is a set used to define a variable with a fixed value that
-can be changed by the user. It is analogous to `MathOptInterface.EqualTo`, but can be used
+A `MOI.Parameter` is a set used to define a variable with a fixed value that
+can be changed by the user. It is analogous to `MOI.EqualTo`, but can be used
 by special methods like the ones in this package to remove the fixed variable from the
 optimization problem. This permits the usage of multiplicative parameters in lienar models
 and might speedup solves since the number of variables is reduced.
 
 ### Adding a new parameter to a model
 
-To add a parameter to a model, we must use the `MOI.add_constrained_variable()` function, passing as its arguments the model and a `MathOptInterface.Parameter` with its given value:
+To add a parameter to a model, we must use the `MOI.add_constrained_variable()` function, passing as its arguments the model and a `MOI.Parameter` with its given value:
 
 ```julia
 y, cy = MOI.add_constrained_variable(optimizer, MOI.Parameter(0.0))
 ```
 
 ### Changing the parameter value
 
-To change a given parameter's value, access its `VariableIndex` and set it to the new value using the `MathOptInterface.Parameter` structure.
+To change a given parameter's value, access its `VariableIndex` and set it to the new value using the `MOI.Parameter` structure.
 
 ```julia
 MOI.set(optimizer, POI.ParameterValue(), y, MOI.Parameter(2.0))