problem

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 Go to latest Published: Oct 15, 2025 License: MIT Imports: 9 Imported by: 0

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Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func ConstraintIsRedundantGivenOthers added in v0.5.5 

func ConstraintIsRedundantGivenOthers(
	constraint symbolic.Constraint,
	constraints []symbolic.Constraint,
) bool

func ToSymbolicConstraint 

func ToSymbolicConstraint(inputConstraint optim.Constraint) (symbolic.Constraint, error)

ToSymbolicConstraint Description: 

Converts a constraint in the form of a optim.Constraint object into a symbolic.Constraint object.

Types

type ObjSense 

type ObjSense string

ObjSense represents whether an optimization objective is to be maximized or minimized. This implementation conforms to the Gurobi encoding 

const (
	SenseMinimize ObjSense = "Minimize"
	SenseMaximize          = "Maximize"
	SenseFind     ObjSense = "Find"
)

Objective senses (minimize and maximize) encoding using Gurobi's standard

func ToObjSense 

func ToObjSense(sense optim.ObjSense) ObjSense

ToObjSense Description: 

This method converts an input optim.ObjSense to a problem.ObjSense.

type Objective 

type Objective struct {
	symbolic.Expression
	Sense ObjSense
}

Objective represents an optimization objective given an expression and objective sense (maximize or minimize).

func NewObjective 

func NewObjective(e symbolic.Expression, sense ObjSense) *Objective

NewObjective returns a new optimization objective given an expression and objective sense

func (*Objective) IsLinear added in v0.5.1 

func (o *Objective) IsLinear() bool

IsLinear Description: 

This method returns true if the objective is linear, false otherwise.

func (*Objective) SubstituteAccordingTo added in v0.5.5 

func (o *Objective) SubstituteAccordingTo(replacementMap map[symbolic.Variable]symbolic.Expression) *Objective

SubstituteAccordingTo Description: 

Substitutes the variables in the objective according to the replacement map.

type OptimizationProblem 

type OptimizationProblem struct {
	Name        string
	Variables   []symbolic.Variable
	Constraints []symbolic.Constraint
	Objective   Objective
}

OptimizationProblem represents the overall constrained linear optimization model to be solved. OptimizationProblem contains all the variables associated with the optimization problem, constraints, objective, and parameters. New variables can only be created using an instantiated OptimizationProblem.

func From 

func From(inputModel optim.Model) (*OptimizationProblem, error)

From Description: 

Converts the given input into an optimization problem.

func GetExampleProblem3 added in v0.5.3 

func GetExampleProblem3() *OptimizationProblem

GetExampleProblem3 Description: 

Returns the LP from this youtube video:
	https://www.youtube.com/watch?v=QAR8zthQypc&t=483s
It should look like this:
	Maximize	4 x1 + 3 x2 + 5 x3
	Subject to
		x1 + 2 x2 + 2 x3 <= 4
		3 x1 + 4 x3 <= 6
		2 x1 + x2 + 4 x3 <= 8
		x1 >= -1.0
		x2 >= -1.0
		x3 >= -1.0

func GetExampleProblem4 added in v0.5.5 

func GetExampleProblem4() *OptimizationProblem

GetExampleProblem4 Description: 

Returns the LP from this youtube video:
	https://www.youtube.com/watch?v=QAR8zthQypc&t=483s
It should look like this:
	Maximize	4 x1 + 3 x2 + 5 x3
	Subject to
		x1 + 2 x2 + 2 x3 <= 4
		3 x1 + 4 x3 <= 6
		2 x1 + x2 + 4 x3 <= 8
		x1 >= 0
		x2 >= 0
		x3 >= 0

func GetExampleProblem5 added in v0.5.5 

func GetExampleProblem5() *OptimizationProblem

GetExampleProblem5 Description: 

Returns the LP from this youtube video:
	https://www.youtube.com/watch?v=QAR8zthQypc&t=483s
It should look like this:
	Maximize	4 x1 + 3 x2 + 5 x3
	Subject to
		x1 + 2 x2 + 2 x3 <= 4
		3 x1 + 4 x3 <= 6
		2 x1 + x2 + 4 x3 <= 8
		x1 >= 0
		x2 >= 0
		x3 >= 0

func NewProblem 

func NewProblem(name string) *OptimizationProblem

NewProblem returns a new model with some default arguments such as not to show the log and no time limit.

func (*OptimizationProblem) AddBinaryVariable 

func (op *OptimizationProblem) AddBinaryVariable() symbolic.Variable

AddBinaryVar adds a binary variable to the model and returns said variable.

func (*OptimizationProblem) AddBinaryVariableMatrix 

func (op *OptimizationProblem) AddBinaryVariableMatrix(rows, cols int) [][]symbolic.Variable

AddBinaryVariableMatrix adds a matrix of binary variables to the model and returns the resulting slice.

func (*OptimizationProblem) AddBinaryVariableVector 

func (op *OptimizationProblem) AddBinaryVariableVector(num int) symbolic.VariableVector

AddBinaryVariableVector adds a vector of binary variables to the model and returns the slice.

func (*OptimizationProblem) AddRealVariable 

func (op *OptimizationProblem) AddRealVariable() symbolic.Variable

AddRealVariable Description: 

Adds a Real variable to the model and returns said variable.

func (*OptimizationProblem) AddVariable 

func (op *OptimizationProblem) AddVariable() symbolic.Variable

AddVariable Description: 

This method adds an "unbounded" continuous variable to the model.

func (*OptimizationProblem) AddVariableClassic 

func (op *OptimizationProblem) AddVariableClassic(lower, upper float64, vtype symbolic.VarType) symbolic.Variable

AddVariable adds a variable of a given variable type to the model given the lower and upper value limits. This variable is returned.

func (*OptimizationProblem) AddVariableMatrix 

func (op *OptimizationProblem) AddVariableMatrix(
	rows, cols int, lower, upper float64, vtype symbolic.VarType,
) symbolic.VariableMatrix

AddVariableMatrix adds a matrix of variables of a given type to the model with lower and upper value limits and returns the resulting slice.

func (*OptimizationProblem) AddVariableVector 

func (op *OptimizationProblem) AddVariableVector(dim int) symbolic.VariableVector

AddVariableVector Description: 

Creates a VarVector object using a constructor that assumes you want an "unbounded" vector of real optimization
variables.

func (*OptimizationProblem) AddVariableVectorClassic 

func (op *OptimizationProblem) AddVariableVectorClassic(
	num int, lower, upper float64, vtype symbolic.VarType,
) symbolic.VariableVector

AddVariableVectorClassic Description: 

The classic version of AddVariableVector defined in the original goop.

func (*OptimizationProblem) Check added in v0.5.1 

func (op *OptimizationProblem) Check() error

Check Description: 

Checks that the OptimizationProblem is valid.

func (*OptimizationProblem) CheckIfLinear added in v0.5.3 

func (op *OptimizationProblem) CheckIfLinear() error

CheckIfLinear Description: 

Checks the current optimization problem to see if it is linear.
Returns an error if the problem is not linear.

func (*OptimizationProblem) Copy added in v0.5.5 

func (op *OptimizationProblem) Copy() *OptimizationProblem

Copy Description: 

Returns a deep copy of the optimization problem.

func (*OptimizationProblem) CopyVariable added in v0.5.5 

func (op *OptimizationProblem) CopyVariable(variable symbolic.Variable) symbolic.Variable

CopyVariable Description: 

Creates a deep copy of the given variable within
the optimization problem.

func (*OptimizationProblem) IsLinear added in v0.5.1 

func (op *OptimizationProblem) IsLinear() bool

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).

func (*OptimizationProblem) LinearEqualityConstraintMatrices added in v0.5.2 

func (op *OptimizationProblem) LinearEqualityConstraintMatrices() (symbolic.KMatrix, symbolic.KVector, error)

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.

func (*OptimizationProblem) LinearInequalityConstraintMatrices added in v0.5.2 

func (op *OptimizationProblem) LinearInequalityConstraintMatrices() (symbolic.KMatrix, symbolic.KVector, error)

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.

func (*OptimizationProblem) MakeNotWellDefinedError added in v0.5.5 

func (op *OptimizationProblem) MakeNotWellDefinedError() ope.NotWellDefinedError

func (*OptimizationProblem) SetObjective 

func (op *OptimizationProblem) SetObjective(e symbolic.Expression, sense ObjSense) error

func (*OptimizationProblem) SimplifyConstraints added in v0.5.5 

func (op *OptimizationProblem) SimplifyConstraints()

SimplifyConstraints Description: 

This method simplifies the constraints of the optimization problem by removing redundant constraints.

func (*OptimizationProblem) String added in v0.6.1 

func (op *OptimizationProblem) String() string

String Description: 

Creates a string for the problem.

func (*OptimizationProblem) ToLPStandardForm1 added in v0.5.3 

func (problemIn *OptimizationProblem) ToLPStandardForm1() (*OptimizationProblem, []symbolic.Variable, map[symbolic.Variable]symbolic.Expression, error)

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).

	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).

Note: 

This method will transform the vector or matrix constraints in the input problem
into a set of scalar constraints. Thus, the number of constraints in your problem may
"seem" to change.

func (*OptimizationProblem) ToLPStandardForm2 added in v0.5.5 

func (problemIn *OptimizationProblem) ToLPStandardForm2() (*OptimizationProblem, []symbolic.Variable, map[symbolic.Variable]symbolic.Expression, error)

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).

func (*OptimizationProblem) ToProblemWithAllPositiveVariables added in v0.5.3 

func (op *OptimizationProblem) ToProblemWithAllPositiveVariables() (*OptimizationProblem, map[symbolic.Variable]symbolic.Expression, error)

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_-).

func (*OptimizationProblem) WithAllPositiveVariableConstraintsRemoved added in v0.5.5 

func (op *OptimizationProblem) WithAllPositiveVariableConstraintsRemoved() *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.

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