Getting Started

Getting Started#

Welcome to the moospread documentation.

This page provides a minimal example showing how to install the package and solve a standard multi-objective optimization problem using the SPREAD solver.

Installation#

Create and activate a new conda environment, then install the package from PyPI:

conda create -n moospread python=3.11
conda activate moospread
pip install moospread

Alternatively, to install the latest development version from GitHub:

conda create -n moospread python=3.11
conda activate moospread
git clone https://github.com/safe-autonomous-systems/moo-spread.git
cd moo-spread
pip install -e .

Basic Usage#

This example shows how to solve an online multi-objective optimization benchmark problem (ZDT2) using the SPREAD solver.

import numpy as np
import torch

# Import the SPREAD solver
from moospread import SPREAD

# Import a test problem
from moospread.tasks import ZDT2

# Define the problem
problem = ZDT2(n_var=30)

# Initialize the SPREAD solver
solver = SPREAD(
    problem,
    data_size=10000,
    timesteps=1000,
    num_epochs=1000,
    train_tol=100,
    mode="online",
    seed=2026,
    verbose=True
)

# Solve the problem
res_x, res_y = solver.solve(
    num_points_sample=200,
    iterative_plot=True,
    plot_period=10,
    max_backtracks=25,
    save_results=True,
    samples_store_path="./samples_dir/",
    images_store_path="./images_dir/"
)

This will train a diffusion-based multi-objective solver, approximate the Pareto front of the ZDT2 problem in the online setting, and store the generated samples and plots in the specified directories.

Next Steps#

To explore more advanced configurations, see:

You can also define your own optimization problem following the guidelines in Problems, if it is not listed in Test Problems: .

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