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adasamp-pareto: adaptive bayes-sampling for multi-criteria optimization
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Adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. The algorithm aims to find the Pareto-optimal solution of
.. math::
max [ y(x) ] s.t. f(x) = feasible
in an iterative procedure. Here, :math:`y(x)` denotes the multi-dimensional goals and :math:`f(x)` the binary feasibility of the problem (in the sense that certain design variables :math:`x` lead to invalid goals). All technical details can be found in the paper "Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification" (`<https://arxiv.org/abs/2008.12005>`_).
**Installation**
Install via ``pip`` or clone this repository. In order to use ``pip``, type:
.. code-block:: sh
$ pip install adasamp-pareto
**Usage**
The class ``AdaptiveSampler`` is used to define and solve a problem instance. Simple example:
.. code-block:: python
from adasamp import AdaptiveSampler
# Create instance
sampler = AdaptiveSampler(func, # Problem definition: function returns (goals Y, feasibility f)
X_limits, # Design variable limits to search solution in
Y_ref, # Reference point, has to be dominated by any goal Y
iterations, # Number of solver iterations
Y_model, # Regression model to predict goals Y
f_model) # Classification model to predict feasibility f
# Return the sampling suggestions X, the corresponding goals Y, and the corresponding feasibilities f.
X, Y, f = sampler.sample()
Demo notebooks can be found in the `examples/` directory.
**Documentation**
Complete documentation is available: `<https://adasamp-pareto.readthedocs.io/en/latest>`_.
📖 **Citation**
If you find this code useful in your research, please consider citing:
.. code-block::
@misc{heesebortzCITE2020,
title={Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification},
author={Raoul Heese and Michael Bortz},
year={2020},
eprint={2008.12005},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
