@types/google-apps-script
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TypeScript definitions for google-apps-script
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TypeScript
/// <reference path="google-apps-script.types.d.ts" />
declare namespace GoogleAppsScript {
namespace Optimization {
/**
* 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).
*
* The example below creates one variable x with values between 0 and 5
* and creates the constraint 0 ≤ 2 * x ≤ 5. This is done by first creating a constraint
* with the lower bound 5 and upper bound 5. Then the coefficient for variable
* x in this constraint is set to 2.
*
* var engine = LinearOptimizationService.createEngine();
* // Create a variable so we can add it to the constraint
* engine.addVariable('x', 0, 5);
* // Create a linear constraint with the bounds 0 and 10
* var constraint = engine.addConstraint(0, 10);
* // Set the coefficient of the variable in the constraint. The constraint is now:
* // 0 <= 2 * x <= 5
* constraint.setCoefficient('x', 2);
*/
interface LinearOptimizationConstraint {
setCoefficient(variableName: string, coefficient: number): LinearOptimizationConstraint;
}
/**
* The engine used to model and solve 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
*
* var engine = 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
* var constraint = engine.addConstraint(0, 10);
* constraint.setCoefficient('x', 2);
* constraint.setCoefficient('y', 5);
*
* // Create the constraint: 0 <= 10 * x + 3 * y <= 20
* var 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
* var solution = engine.solve();
* if (!solution.isValid()) {
* Logger.log('No solution ' + solution.getStatus());
* } else {
* Logger.log('Value of x: ' + solution.getVariableValue('x'));
* Logger.log('Value of y: ' + solution.getVariableValue('y'));
* }
*/
interface LinearOptimizationEngine {
addConstraint(lowerBound: number, upperBound: number): LinearOptimizationConstraint;
addConstraints(
lowerBounds: number[],
upperBounds: number[],
variableNames: string[][],
coefficients: number[][],
): LinearOptimizationEngine;
addVariable(name: string, lowerBound: number, upperBound: number): LinearOptimizationEngine;
addVariable(
name: string,
lowerBound: number,
upperBound: number,
type: VariableType,
): LinearOptimizationEngine;
addVariable(
name: string,
lowerBound: number,
upperBound: number,
type: VariableType,
objectiveCoefficient: number,
): LinearOptimizationEngine;
addVariables(
names: string[],
lowerBounds: number[],
upperBounds: number[],
types: VariableType[],
objectiveCoefficients: number[],
): LinearOptimizationEngine;
setMaximization(): LinearOptimizationEngine;
setMinimization(): LinearOptimizationEngine;
setObjectiveCoefficient(variableName: string, coefficient: number): LinearOptimizationEngine;
solve(): LinearOptimizationSolution;
solve(seconds: number): LinearOptimizationSolution;
}
/**
* The linear optimization service, used to model and solve linear and mixed-integer linear
* programs. 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
*
* var engine = LinearOptimizationService.createEngine();
*
* // Add variables, constraints and define the objective using 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
* var constraint = engine.addConstraint(0, 10);
* constraint.setCoefficient('x', 2);
* constraint.setCoefficient('y', 5);
*
* // Create the constraint: 0 <= 10 * x + 3 * y <= 20
* var 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
* var solution = engine.solve();
* if (!solution.isValid()) {
* Logger.log('No solution ' + solution.getStatus());
* } else {
* Logger.log('Value of x: ' + solution.getVariableValue('x'));
* Logger.log('Value of y: ' + solution.getVariableValue('y'));
* }
*/
interface LinearOptimizationService {
Status: typeof Status;
VariableType: typeof VariableType;
createEngine(): LinearOptimizationEngine;
}
/**
* 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
*
* var engine = 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
* var constraint = engine.addConstraint(0, 10);
* constraint.setCoefficient('x', 2);
* constraint.setCoefficient('y', 5);
*
* // Create the constraint: 0 <= 10 * x + 3 * y <= 20
* var 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
* var solution = 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'));
* }
*/
interface LinearOptimizationSolution {
getObjectiveValue(): number;
getStatus(): Status;
getVariableValue(variableName: string): number;
isValid(): boolean;
}
/**
* Status of the solution. Before solving a problem the status will be NOT_SOLVED;
* afterwards it will take any of the other values depending if it successfully found a solution and
* if the solution is optimal.
*/
enum Status {
OPTIMAL,
FEASIBLE,
INFEASIBLE,
UNBOUNDED,
ABNORMAL,
MODEL_INVALID,
NOT_SOLVED,
}
/**
* Type of variables created by the engine.
*/
enum VariableType {
INTEGER,
CONTINUOUS,
}
}
}
declare var LinearOptimizationService: GoogleAppsScript.Optimization.LinearOptimizationService;