ParticleMethodsForMultiObjectiveOptimization

This is the repository for the AISTATS-24 paper Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow.

The implementation is based on the official implementation for COSMOS: a method to learn Pareto fronts that scales to large datasets and deep models, including scripts to reproduce experiments for profiling the Pareto front in Multi-Objective Optimization (MOO).

Usage

1. Download the dataset as described in readme.md in the respective data folder.
2. Run the code:

python multi_objective/main.py --dataset mslr --method particle

For the logs and results see newly created folder results.

For the settings see settings.py

---

Available datasets:

command-line option	Description
-d mslr	MSLR-WEB10K dataset
-d zdt1	ZDT1 Problem
-d zdt2	ZDT2 Problem
-d zdt3	ZDT3 Problem
-d dtlz7	DTLZ7 Problem

---

Available algorithms:

command-line option	Description
-m cosmos	COSMOS algorithm [1]
-m hyper_ln	PHN (Linear Scalarization) algorithm [2]
-m hyper_epo	PHN (EPO) algorithm [2]
-m pmtl	ParetoMTL algorithm [3]
-m single_task	Treat each objective as single task
-m uniform	Uniform scaling of all objectives
-m argmo_hv	ARGMO wrt hypervolume [4]
-m argmo_kernel	ARGMO with kernel [4]
-m particle	Particle methods with Wasserstein-Fisher-Rao flow

[1] Ruchte, Michael, and Josif Grabocka. "Scalable pareto front approximation for deep multi-objective learning." 2021 IEEE international conference on data mining (ICDM). IEEE, 2021.

[2] Navon, Aviv, et al. "Learning the Pareto Front with Hypernetworks." International Conference on Learning Representations. 2020.

[3] Lin, Xi, et al. "Pareto multi-task learning." Advances in neural information processing systems 32 (2019).

[4] Chen, Weiyu, and James Kwok. "Multi-Objective Deep Learning with Adaptive Reference Vectors." Advances in Neural Information Processing Systems 35 (2022): 32723-32735.

[5] Liu, Xingchao, Xin Tong, and Qiang Liu. "Profiling pareto front with multi-objective stein variational gradient descent." Advances in Neural Information Processing Systems 34 (2021): 14721-14733.

Installation

Requirements:

1. CUDA capable GPU

Create a venv:

python3 -m venv moo
source moo/bin/activate

Clone repository:

git clone ...
cd moo

Install requirements:

pip install -r requirements.txt

The large number of dependencies is partly due to the baselines, available in this repository as well.

Acknowledgments

We thank the authors of [1] for creating a helpful code framework to compare different algorithms. We are also thankful to the authors of [4, 5] for sharing their code, contributing significantly to our research.