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Refactor docstrings in optimization_problem.go to remove redundant formatting #33

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Merged
kwesiRutledge merged 2 commits into from
Feb 18, 2026
Merged

Refactor docstrings in optimization_problem.go to remove redundant formatting #33

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Copilot Feb 18, 2026
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Refactor docstrings in optimization_problem.go to remove method names…
Copilot Feb 18, 2026
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213 changes: 73 additions & 140 deletions problem/optimization_problem.go
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Original file line number Diff line number Diff line change
Expand Up @@ -30,20 +30,14 @@ func NewProblem(name string) *OptimizationProblem {
}

/*
AddVariable
Description:

This method adds an "unbounded" continuous variable to the model.
This method adds an "unbounded" continuous variable to the model.
*/
func (op *OptimizationProblem) AddVariable() symbolic.Variable {
return op.AddRealVariable()
}

/*
AddRealVariable
Description:

Adds a Real variable to the model and returns said variable.
Adds a Real variable to the model and returns said variable.
*/
func (op *OptimizationProblem) AddRealVariable() symbolic.Variable {
return op.AddVariableClassic(-optim.INFINITY, optim.INFINITY, symbolic.Continuous)
Expand Down Expand Up @@ -71,11 +65,8 @@ func (op *OptimizationProblem) AddBinaryVariable() symbolic.Variable {
}

/*
AddVariableVector
Description:

Creates a VarVector object using a constructor that assumes you want an "unbounded" vector of real optimization
variables.
Creates a VarVector object using a constructor that assumes you want an "unbounded" vector of real optimization
variables.
*/
func (op *OptimizationProblem) AddVariableVector(dim int) symbolic.VariableVector {
// Constants
Expand All @@ -89,10 +80,7 @@ func (op *OptimizationProblem) AddVariableVector(dim int) symbolic.VariableVecto
}

/*
AddVariableVectorClassic
Description:

The classic version of AddVariableVector defined in the original goop.
The classic version of AddVariableVector defined in the original goop.
*/
func (op *OptimizationProblem) AddVariableVectorClassic(
num int, lower, upper float64, vtype symbolic.VarType,
Expand Down Expand Up @@ -141,10 +129,9 @@ func (op *OptimizationProblem) AddBinaryVariableMatrix(rows, cols int) [][]symbo
}

/*
SetObjective
Description:
sets the objective of the model given an expression and
objective sense.
Sets the objective of the model given an expression and
objective sense.

Notes:
To make this function easier to parse, we will assume an expression
is given, even though objectives are normally scalars.
Expand All @@ -163,10 +150,7 @@ func (op *OptimizationProblem) SetObjective(e symbolic.Expression, sense ObjSens
}

/*
ToSymbolicConstraint
Description:

Converts a constraint in the form of a optim.Constraint object into a symbolic.Constraint object.
Converts a constraint in the form of a optim.Constraint object into a symbolic.Constraint object.
*/
func ToSymbolicConstraint(inputConstraint optim.Constraint) (symbolic.Constraint, error) {
// Input Processing
Expand Down Expand Up @@ -207,10 +191,7 @@ func ToSymbolicConstraint(inputConstraint optim.Constraint) (symbolic.Constraint
}

/*
From
Description:

Converts the given input into an optimization problem.
Converts the given input into an optimization problem.
*/
func From(inputModel optim.Model) (*OptimizationProblem, error) {
// Create a new optimization problem
Expand Down Expand Up @@ -273,10 +254,7 @@ func From(inputModel optim.Model) (*OptimizationProblem, error) {
}

/*
Check
Description:

Checks that the OptimizationProblem is valid.
Checks that the OptimizationProblem is valid.
*/
func (op *OptimizationProblem) Check() error {
// Check Objective
Expand Down Expand Up @@ -310,13 +288,10 @@ func (op *OptimizationProblem) Check() error {
}

/*
IsLinear
Description:

Checks if the optimization problem is linear.
Per the definition of a linear optimization problem, the problem is linear if and only if:
1. The objective function is linear (i.e., a constant or an affine combination of variables).
2. All constraints are linear (i.e., an affine combination of variables in an inequality or equality).
Checks if the optimization problem is linear.
Per the definition of a linear optimization problem, the problem is linear if and only if:
1. The objective function is linear (i.e., a constant or an affine combination of variables).
2. All constraints are linear (i.e., an affine combination of variables in an inequality or equality).
*/
func (op *OptimizationProblem) IsLinear() bool {
// Run the check method
Expand Down Expand Up @@ -346,14 +321,11 @@ func (op *OptimizationProblem) IsLinear() bool {
}

/*
LinearInequalityConstraintMatrices
Description:

Returns the linear INEQUALITY constraint matrices and vectors.
For all linear inequality constraints, we assemble them into the form:
Ax <= b
Where A is the matrix of coefficients, x is the vector of variables, and b is the vector of constants.
We return A and b.
Returns the linear INEQUALITY constraint matrices and vectors.
For all linear inequality constraints, we assemble them into the form:
Ax <= b
Where A is the matrix of coefficients, x is the vector of variables, and b is the vector of constants.
We return A and b.
*/
func (op *OptimizationProblem) LinearInequalityConstraintMatrices() (symbolic.KMatrix, symbolic.KVector, error) {
// Setup
Expand Down Expand Up @@ -452,14 +424,11 @@ func (op *OptimizationProblem) LinearInequalityConstraintMatrices() (symbolic.KM
}

/*
LinearEqualityConstraintMatrices
Description:

Returns the linear EQUALITY constraint matrices and vectors.
For all linear equality constraints, we assemble them into the form:
Cx = d
Where C is the matrix of coefficients, x is the vector of variables, and d is the vector of constants.
We return C and d.
Returns the linear EQUALITY constraint matrices and vectors.
For all linear equality constraints, we assemble them into the form:
Cx = d
Where C is the matrix of coefficients, x is the vector of variables, and d is the vector of constants.
We return C and d.
*/
func (op *OptimizationProblem) LinearEqualityConstraintMatrices() (symbolic.KMatrix, symbolic.KVector, error) {
// Setup
Expand Down Expand Up @@ -567,15 +536,12 @@ func (op *OptimizationProblem) LinearEqualityConstraintMatrices() (symbolic.KMat
}

/*
ToProblemWithAllPositiveVariables
Description:

Transforms the given optimization problem into a new optimization problem
that only contains positive variables.
In math, this means that we will create two new variables (x_+ and x_-) for each
original variable (x), one for the positive part and one for the negative part.
Then, we replace every instance of the original variable with the difference
of the two new variables (x = x_+ - x_-).
Transforms the given optimization problem into a new optimization problem
that only contains positive variables.
In math, this means that we will create two new variables (x_+ and x_-) for each
original variable (x), one for the positive part and one for the negative part.
Then, we replace every instance of the original variable with the difference
of the two new variables (x = x_+ - x_-).
*/
func (op *OptimizationProblem) ToProblemWithAllPositiveVariables() (*OptimizationProblem, map[symbolic.Variable]symbolic.Expression, error) {
// Setup
Expand Down Expand Up @@ -644,20 +610,17 @@ func (op *OptimizationProblem) ToProblemWithAllPositiveVariables() (*Optimizatio
}

/*
ToLPStandardForm1
Description:
Transforms the given linear program (represented in an OptimizationProblem object)
into a standard form (i.e., only linear equality constraints and a linear objective function).

Transforms the given linear program (represented in an OptimizationProblem object)
into a standard form (i.e., only linear equality constraints and a linear objective function).
sense c^T * x
subject to
A * x = b
x >= 0

sense c^T * x
subject to
A * x = b
x >= 0

Where A is a matrix of coefficients, b is a vector of constants, and c is the vector of coefficients
for the objective function. This method also returns the slack variables (i.e., the variables that
are added to the problem to convert the inequalities into equalities).
Where A is a matrix of coefficients, b is a vector of constants, and c is the vector of coefficients
for the objective function. This method also returns the slack variables (i.e., the variables that
are added to the problem to convert the inequalities into equalities).

Note:

Expand Down Expand Up @@ -782,23 +745,20 @@ func (problemIn *OptimizationProblem) ToLPStandardForm1() (*OptimizationProblem,
}

/*
ToLPStandardForm2
Description:

Transforms the given linear program (represented in an OptimizationProblem object)
into a standard form (i.e., only linear equality constraints and a linear objective function).

max c^T * x
subject to
A * x = b
x >= 0

Where:
- A is a matrix of coefficients,
- b is a vector of constants, and
- c is the vector of coefficients for the objective function.
This method also returns the slack variables (i.e., the variables that
are added to the problem to convert the inequalities into equalities).
Transforms the given linear program (represented in an OptimizationProblem object)
into a standard form (i.e., only linear equality constraints and a linear objective function).

max c^T * x
subject to
A * x = b
x >= 0

Where:
- A is a matrix of coefficients,
- b is a vector of constants, and
- c is the vector of coefficients for the objective function.
This method also returns the slack variables (i.e., the variables that
are added to the problem to convert the inequalities into equalities).
*/
func (problemIn *OptimizationProblem) ToLPStandardForm2() (*OptimizationProblem, []symbolic.Variable, map[symbolic.Variable]symbolic.Expression, error) {
// Input Processing
Expand Down Expand Up @@ -830,15 +790,12 @@ func (problemIn *OptimizationProblem) ToLPStandardForm2() (*OptimizationProblem,
}

/*
WithAllPositiveVariableConstraintsRemoved
Description:

Returns a new optimization problem that is the same as the original problem
but with all constraints of the following form removed:
x >= 0
0 <= x
Where x is a variable in the problem.
This is useful for removing redundant constraints that are already implied by the variable bounds.
Returns a new optimization problem that is the same as the original problem
but with all constraints of the following form removed:
x >= 0
0 <= x
Where x is a variable in the problem.
This is useful for removing redundant constraints that are already implied by the variable bounds.
*/
func (op *OptimizationProblem) WithAllPositiveVariableConstraintsRemoved() *OptimizationProblem {
// Setup
Expand Down Expand Up @@ -869,11 +826,8 @@ func (op *OptimizationProblem) WithAllPositiveVariableConstraintsRemoved() *Opti
}

/*
CheckIfLinear
Description:

Checks the current optimization problem to see if it is linear.
Returns an error if the problem is not linear.
Checks the current optimization problem to see if it is linear.
Returns an error if the problem is not linear.
*/
func (op *OptimizationProblem) CheckIfLinear() error {
// Input Processing
Expand Down Expand Up @@ -912,11 +866,8 @@ func (op *OptimizationProblem) CheckIfLinear() error {
}

/*
CopyVariable
Description:

Creates a deep copy of the given variable within
the optimization problem.
Creates a deep copy of the given variable within
the optimization problem.
*/
func (op *OptimizationProblem) CopyVariable(variable symbolic.Variable) symbolic.Variable {
// Setup
Expand All @@ -938,10 +889,7 @@ func (op *OptimizationProblem) CopyVariable(variable symbolic.Variable) symbolic
}

/*
Copy
Description:

Returns a deep copy of the optimization problem.
Returns a deep copy of the optimization problem.
*/
func (op *OptimizationProblem) Copy() *OptimizationProblem {
// Setup
Expand All @@ -968,10 +916,7 @@ func (op *OptimizationProblem) Copy() *OptimizationProblem {
}

/*
SimplifyConstraints
Description:

This method simplifies the constraints of the optimization problem by removing redundant constraints.
This method simplifies the constraints of the optimization problem by removing redundant constraints.
*/
func (op *OptimizationProblem) SimplifyConstraints() {
// Setup
Expand Down Expand Up @@ -1001,10 +946,7 @@ func (op *OptimizationProblem) MakeNotWellDefinedError() ope.NotWellDefinedError
}

/*
String
Description:

Creates a string for the problem.
Creates a string for the problem.
*/
func (op *OptimizationProblem) String() string {
// Create string for the objective
Expand Down Expand Up @@ -1041,22 +983,16 @@ func (op *OptimizationProblem) String() string {
}

/*
GetName
Description:

Returns the name of the optimization problem.
(Necessary for implementing the symbolic.Environment interface).
Returns the name of the optimization problem.
(Necessary for implementing the symbolic.Environment interface).
*/
func (op *OptimizationProblem) GetName() string {
return op.Name
}

/*
TrackVariable
Description:

Adds the given variable to the optimization problem if it is not already present.
Returns true if the variable was added, false if it was already present.
Adds the given variable to the optimization problem if it is not already present.
Returns true if the variable was added, false if it was already present.
*/
func (op *OptimizationProblem) TrackVariable(v symbolic.Variable) bool {
// Check if the variable is already present
Expand All @@ -1072,11 +1008,8 @@ func (op *OptimizationProblem) TrackVariable(v symbolic.Variable) bool {
}

/*
AllTrackedVariables
Description:

Returns a slice of all variables that are tracked by the optimization problem.
(Necessary for implementing the symbolic.Environment interface).
Returns a slice of all variables that are tracked by the optimization problem.
(Necessary for implementing the symbolic.Environment interface).
*/
func (op *OptimizationProblem) AllTrackedVariables() []symbolic.Variable {
return op.Variables
Expand Down