Initialize Inverse Optimization

Initializes an inverse optimization process for a trial in globalMOO.

Endpoint

POST /trials/{trial_id}/initialize-inverse

Request Parameters

Parameter	Type	Required	Description
convergence	float	Yes	Convergence criterion (must be positive)
objectives	array[float]	Yes	Target values for each objective
objectiveTypes	array[string]	Yes	Type for each objective (see valid types below)
initialInput	array[float]	Yes	Initial input values (must match project input count)
initialOutput	array[float]	Yes	Initial output values (must match objectives length)
minimumBounds	array[float]	Yes	Lower bounds (for percent/value objectives)
maximumBounds	array[float]	Yes	Upper bounds (for percent/value objectives)
desiredL1Norm	float	Yes	Desired L1 norm (defaults to 0.0)

Request Format

{
    "convergence": 0.001,
    "objectives": [100.0, 50.0, 25.0],
    "objectiveTypes": ["percent", "value", "percent"],
    "initialInput": [1.0, 2.0, 3.0],
    "initialOutput": [90.0, 45.0, 20.0],
    "minimumBounds": [-5.0, -2.0, -1.0],
    "maximumBounds": [5.0, 2.0, 1.0],
    "desiredL1Norm": 0.0
}

Response Format

{
    "id": 123,
    "trialId": 456,
    "status": "active",
    "convergence": 0.001,
    "iteration": 1,
    "input": [1.0, 2.0, 3.0],
    "output": [90.0, 45.0, 20.0],
    "suggestTime": 0.0,
    "computeTime": 0.0,
    "createdAt": "2025-01-31T10:00:00.000Z",
    "updatedAt": "2025-01-31T10:00:00.000Z",
    "results": [
        {
            "number": 0,
            "type": "percent",
            "objective": 100.0,
            "minimumBound": -5.0,
            "maximumBound": 5.0,
            "output": 90.0,
            "error": 10.0,
            "detail": "Error exceeds percent bounds",
            "satisfied": false
        },
        {
            "number": 1,
            "type": "value",
            "objective": 50.0,
            "minimumBound": -2.0,
            "maximumBound": 2.0,
            "output": 45.0,
            "error": -5.0,
            "detail": "Output within bounds",
            "satisfied": true
        },
        {
            "number": 2,
            "type": "percent",
            "objective": 25.0,
            "minimumBound": -1.0,
            "maximumBound": 1.0,
            "output": 20.0,
            "error": -20.0,
            "detail": "Error exceeds percent bounds",
            "satisfied": false
        }
    ]
}

Examples

Python

from globalmoo import Client, ObjectiveType

client = Client(api_key="your-api-key")
inverse = client.initialize_inverse(
    trial_id=123,
    convergence=0.001,
    objectives=[100.0, 50.0, 25.0],
    objective_types=[
        ObjectiveType.PERCENT,
        ObjectiveType.VALUE,
        ObjectiveType.PERCENT
    ],
    initial_input=[1.0, 2.0, 3.0],
    initial_output=[90.0, 45.0, 20.0],
    minimum_bounds=[-5.0, -2.0, -1.0],
    maximum_bounds=[5.0, 2.0, 1.0],
    desired_l1_norm=0.0
)

Node.js

const { Client } = require('globalmoo');

const client = new Client('your-api-key');
const inverse = await client.initializeInverse({
    trialId: 123,
    convergence: 0.001,
    objectives: [100.0, 50.0, 25.0],
    objectiveTypes: ["exact", "value", "percent"],
    initialInput: [1.0, 2.0, 3.0],
    initialOutput: [90.0, 45.0, 20.0],
    minimumBounds: [-5.0, -2.0, -1.0],
    maximumBounds: [5.0, 2.0, 1.0],
    desiredL1Norm: 0.0
});

PHP

$client = new Client("your-api-key");

$inverse = $client->initializeInverse([
    "trialId" => 123,
    "convergence" => 0.001,
    "objectives" => [100.0, 50.0, 25.0],
    "objectiveTypes" => ["exact", "value", "percent"],
    "initialInput" => [1.0, 2.0, 3.0],
    "initialOutput" => [90.0, 45.0, 20.0],
    "minimumBounds" => [-5.0, -2.0, -1.0],
    "maximumBounds" => [5.0, 2.0, 1.0],
    "desiredL1Norm" => 0.0
]);

Error Responses

Status Code	Description
400	Invalid request - Missing required fields or invalid format
401	Unauthorized - Invalid API key
404	Trial not found
429	Too many requests - Rate limit exceeded
500	Internal server error

Validation Rules

• All numeric arrays (objectives, initialInput, initialOutput) must be arrays of floats

• Array lengths must be consistent:

  • objectives and objectiveTypes must have the same length

  • initialInput length must match the project's input count

  • initialOutput length must match objectives length

  • minimumBounds/maximumBounds must match objectives length when required

• Convergence must be a positive float

• objectiveTypes must contain valid type strings (see above)

• For PERCENT and VALUE objectives:

  • minimumBounds and maximumBounds are required

  • maximumBounds values must be greater than minimumBounds

• For EXACT objectives, convergence must be positive, and all entries must be EXACT. This objective type refers to the total L1-norm for the outcomes vector, not any specific outcome.

• All input and output values must be valid floating point numbers