Optimization Service Stay organized with collections Save and categorize content based on your preferences.

Page Summary

The linear optimization service is used to model and solve linear and mixed-integer linear programs.
Key classes for this service include LinearOptimizationConstraint, LinearOptimizationEngine, LinearOptimizationService, and LinearOptimizationSolution.
The LinearOptimizationEngine is used to build the linear program model by adding constraints and variables, and setting the optimization direction.
The LinearOptimizationSolution provides methods to retrieve the objective value, the status of the solution, and the values of the variables.
The Status property indicates the result of the solve operation, such as OPTIMAL, FEASIBLE, or INFEASIBLE.

Optimization

The linear optimization service, used to model and solve linear and mixed-integer linear programs.

Classes

Name	Brief description
`LinearOptimizationConstraint`	Object storing a linear constraint of the form `lowerBound ≤ Sum(a(i) x(i)) ≤ upperBound` where `lowerBound` and `upperBound` are constants, `a(i)` are constant coefficients and `x(i)` are variables (unknowns).
`LinearOptimizationEngine`	The engine used to model and solve a linear program.
`LinearOptimizationService`	The linear optimization service, used to model and solve linear and mixed-integer linear programs.
`LinearOptimizationSolution`	The solution of a linear program.
`Status`	Status of the solution.
`VariableType`	Type of variables created by the engine.

`LinearOptimizationConstraint`

Methods

Method	Return type	Brief description
`setCoefficient(variableName, coefficient)`	`LinearOptimizationConstraint`	Sets the coefficient of a variable in the constraint.

`LinearOptimizationEngine`

Methods

Method	Return type	Brief description
`addConstraint(lowerBound, upperBound)`	`LinearOptimizationConstraint`	Adds a new linear constraint in the model.
`addConstraints(lowerBounds, upperBounds, variableNames, coefficients)`	`LinearOptimizationEngine`	Adds constraints in batch to the model.
`addVariable(name, lowerBound, upperBound)`	`LinearOptimizationEngine`	Adds a new continuous variable to the model.
`addVariable(name, lowerBound, upperBound, type)`	`LinearOptimizationEngine`	Adds a new variable to the model.
`addVariable(name, lowerBound, upperBound, type, objectiveCoefficient)`	`LinearOptimizationEngine`	Adds a new variable to the model.
`addVariables(names, lowerBounds, upperBounds, types, objectiveCoefficients)`	`LinearOptimizationEngine`	Adds variables in batch to the model.
`setMaximization()`	`LinearOptimizationEngine`	Sets the optimization direction to maximizing the linear objective function.
`setMinimization()`	`LinearOptimizationEngine`	Sets the optimization direction to minimizing the linear objective function.
`setObjectiveCoefficient(variableName, coefficient)`	`LinearOptimizationEngine`	Sets the coefficient of a variable in the linear objective function.
`solve()`	`LinearOptimizationSolution`	Solves the current linear program with the default deadline of 30 seconds.
`solve(seconds)`	`LinearOptimizationSolution`	Solves the current linear program.

`LinearOptimizationService`

Properties

Property	Type	Description
`Status`	`Status`	Status of the solver.
`VariableType`	`VariableType`	Type of variables created by the solver.

Methods

Method	Return type	Brief description
`createEngine()`	`LinearOptimizationEngine`	Creates an engine to to solve linear programs (potentially mixed-integer programs).

`LinearOptimizationSolution`

Methods

Method	Return type	Brief description
`getObjectiveValue()`	`Number`	Gets the value of the objective function in the current solution.
`getStatus()`	`Status`	Gets the status of the solution.
`getVariableValue(variableName)`	`Number`	Gets the value of a variable in the solution created by the last call to `LinearOptimizationEngine.solve()`.
`isValid()`	`Boolean`	Determines whether the solution is either feasible or optimal.

`Status`

Properties

Property	Type	Description
`OPTIMAL`	`Enum`	Status when an optimal solution has been found.
`FEASIBLE`	`Enum`	Status when a feasible (not necessarily optimal) solution has been found.
`INFEASIBLE`	`Enum`	Status when the current model is unfeasible (has no solution).
`UNBOUNDED`	`Enum`	Status when the current model is unbound.
`ABNORMAL`	`Enum`	Status when it failed to find a solution for unexpected reasons.
`MODEL_INVALID`	`Enum`	Status when the model is invalid.
`NOT_SOLVED`	`Enum`	Status when `LinearOptimizationEngine.solve()` has not been called yet.

`VariableType`

Properties

Property	Type	Description
`INTEGER`	`Enum`	Type of variable that can only take integer values.
`CONTINUOUS`	`Enum`	Type of variable that can take any real value.

GeezerVison

Optimization Service Stay organized with collections Save and categorize content based on your preferences.

Page Summary

Classes

LinearOptimizationConstraint

Methods

LinearOptimizationEngine

Methods

LinearOptimizationService

Properties

Methods

LinearOptimizationSolution

Methods

Status

Properties

VariableType

Properties

`LinearOptimizationConstraint`

`LinearOptimizationEngine`

`LinearOptimizationService`

`LinearOptimizationSolution`

`Status`

`VariableType`