Class LinearOptimizationSolution
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AI-generated Key Takeaways
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LinearOptimizationSolutionrepresents the solution of a linear program. -
The solution provides methods to get the objective value, the value of specific variables, the status of the solution, and to check its validity.
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An example demonstrates how to define variables, constraints, and an objective function, solve the linear program, and then retrieve the solution details using the provided methods.
The solution of a linear program. The example below solves the following linear program:
Two variables, x and y:
0 ≤ x ≤ 10
0 ≤ y ≤ 5
Constraints:
0 ≤ 2 * x + 5 * y ≤ 10
0 ≤ 10 * x + 3 * y ≤ 20
Objective:
Maximize x + y
constengine=LinearOptimizationService.createEngine(); // Add variables, constraints and define the objective with addVariable(), // addConstraint(), etc. Add two variables, 0 <= x <= 10 and 0 <= y <= 5 engine.addVariable('x',0,10); engine.addVariable('y',0,5); // Create the constraint: 0 <= 2 * x + 5 * y <= 10 letconstraint=engine.addConstraint(0,10); constraint.setCoefficient('x',2); constraint.setCoefficient('y',5); // Create the constraint: 0 <= 10 * x + 3 * y <= 20 constraint=engine.addConstraint(0,20); constraint.setCoefficient('x',10); constraint.setCoefficient('y',3); // Set the objective to be x + y engine.setObjectiveCoefficient('x',1); engine.setObjectiveCoefficient('y',1); // Engine should maximize the objective engine.setMaximization(); // Solve the linear program constsolution=engine.solve(); if(!solution.isValid()){ Logger.log(`No solution ${solution.getStatus()}`); }else{ Logger.log(`Objective value: ${solution.getObjectiveValue()}`); Logger.log(`Value of x: ${solution.getVariableValue('x')}`); Logger.log(`Value of y: ${solution.getVariableValue('y')}`); }
Methods
| Method | Return type | Brief description |
|---|---|---|
get | Number | Gets the value of the objective function in the current solution. |
get | Status | Gets the status of the solution. |
get | Number | Gets the value of a variable in the solution created by the last call to Linear. |
is | Boolean | Determines whether the solution is either feasible or optimal. |
Detailed documentation
getObjectiveValue()
Gets the value of the objective function in the current solution.
constengine=LinearOptimizationService.createEngine(); // Add variables, constraints and define the objective with addVariable(), // addConstraint(), etc engine.addVariable('x',0,10); // ... // Solve the linear program constsolution=engine.solve(); Logger.log(`ObjectiveValue: ${solution.getObjectiveValue()}`);
Return
Number — the value of the objective function
getStatus()
Gets the status of the solution. Before solving a problem, the status will be NOT_SOLVED.
constengine=LinearOptimizationService.createEngine(); // Add variables, constraints and define the objective with addVariable(), // addConstraint(), etc engine.addVariable('x',0,10); // ... // Solve the linear program constsolution=engine.solve(); conststatus=solution.getStatus(); if(status!==LinearOptimizationService.Status.FEASIBLE&& status!==LinearOptimizationService.Status.OPTIMAL){ throw`No solution ${status}`; } Logger.log(`Status: ${status}`);
Return
Status — the status of the solver
getVariableValue(variableName)
Gets the value of a variable in the solution created by the last call to Linear.
constengine=LinearOptimizationService.createEngine(); // Add variables, constraints and define the objective with addVariable(), // addConstraint(), etc engine.addVariable('x',0,10); // ... // Solve the linear program constsolution=engine.solve(); Logger.log(`Value of x: ${solution.getVariableValue('x')}`);
Parameters
| Name | Type | Description |
|---|---|---|
variable | String | name of the variable |
Return
Number — the value of the variable in the solution
isValid()
Determines whether the solution is either feasible or optimal.
constengine=LinearOptimizationService.createEngine(); // Add variables, constraints and define the objective with addVariable(), // addConstraint(), etc engine.addVariable('x',0,10); // ... // Solve the linear program constsolution=engine.solve(); if(!solution.isValid()){ throw`No solution ${solution.getStatus()}`; }
Return
Boolean — true if the solution is valid (Status.FEASIBLE or
Status.OPTIMAL ); false if not