Skip to content

Running Optimization

GraphMDO allows you to run optimization tasks either locally using the Python API or remotely via microservices.

Using Microservices

The microservices architecture decouples the graph management, execution, and optimization logic.

1. Start Services

Ensure all services are running:

docker-compose up -d

2. Define Problem (Graph Service)

Use the Graph Service API to build your problem graph.

# Clear Graph
curl -X POST http://localhost:8001/clear

# Add Variables
curl -X POST http://localhost:8001/variables -d '{"name": "x", "lower": 0.0, "upper": 10.0}' -H "Content-Type: application/json"
curl -X POST http://localhost:8001/variables -d '{"name": "y", "lower": 0.0, "upper": 10.0}' -H "Content-Type: application/json"
curl -X POST http://localhost:8001/variables -d '{"name": "f_xy"}' -H "Content-Type: application/json"

# Add Tool
curl -X POST http://localhost:8001/tools -d '{"name": "Paraboloid"}' -H "Content-Type: application/json"

# Connect
curl -X POST http://localhost:8001/connections/input -d '{"source": "x", "target": "Paraboloid"}' -H "Content-Type: application/json"
curl -X POST http://localhost:8001/connections/input -d '{"source": "y", "target": "Paraboloid"}' -H "Content-Type: application/json"
curl -X POST http://localhost:8001/connections/output -d '{"source": "Paraboloid", "target": "f_xy"}' -H "Content-Type: application/json"

3. Run Optimization (Optimization Service)

Send an optimization request. The Optimization Service will coordinate with the Execution Service (which runs the tool) and Graph Service (for schema).

curl -X POST http://localhost:8003/optimize \
     -H "Content-Type: application/json" \
     -d '{
           "parameters": [
               {"name": "x", "type": "range", "bounds": [0.0, 10.0]},
               {"name": "y", "type": "range", "bounds": [0.0, 10.0]}
           ],
           "objectives": [
               {"name": "f_xy", "minimize": true}
           ],
           "n_steps": 10
         }'

You will receive a JSON response with the optimization history and best results.