Python package for Polarized Consensus Based Optimization

:snowflake: Polarized Consensus-Based Optimization and Sampling

This code produces the examples for the paper "Polarized consensus-based dynamics for optimization and sampling": https://arxiv.org/abs/2211.05238

@online{bungert2022polarized,
author = {Bungert, Leon and Roith, Tim and Wacker, Philipp},
title = {Polarized consensus-based dynamics for optimization and sampling},
year = {2022},
eprint={2211.05238},
archivePrefix={arXiv},
primaryClass={math.OC}
}


💡 What is PolarCBO/CBS?

Polarized consensus-based dynamics allow to apply consensus-based optimization (CBO) and sampling (CBS) for objective functions with several global minima or distributions with many modes, respectively. Here we have

• particles ${x^{(i)}}\in\mathbb{R}^d$ which explore the space,
• the objective $V:\mathbb{R}^d\to\mathbb{R}$ which we want to optimize.

For optimizing $V$ the position of the particles are updated via the stochastic ODE

\begin{align} \boxed{% d x^{(i)} = -(x^{(i)} - m(x^{(i)})) d t + \sigma |x^{(i)} - m(x^{(i)})| d W^{(i)} } \end{align}

where

• $m(x^{(i)})$ is a weighted empirical mean associated with the point $x^{(i)}$,
• $W^{(i)}$ are independent Brownian motions,
• $\sigma$ scales the influence of the noise term.

For sampling from $\exp(-V)$ the position of the particles are updated via the stochastic ODE

\begin{align} \boxed{% d x^{(i)} = -(x^{(i)} - m(x^{(i)})) d t + \sqrt{2\lambda^{-1}C(x^{(i)})} d W^{(i)} } \end{align}

where $C(x^{(i)})$ is a weighted empirical covariance matrix associated with the point $x^{(i)}$.

The choice of the functions $m(\dot)$ and $C(\cdot)$ are at the heart of our polarized methods. Given a similarity measure $\mathsf k(\cdot,\cdot)$ and an inverse temperature parameter $\beta>0$ we define

\begin{align} m(x) &:= \frac{\sum_{i}\mathsf k(x,x^{(i)})x^{(i)}\exp(-\beta V(x^{(i)}))}{\sum_{i}\mathsf k(x,x^{(i)})\exp(-\beta V(x^{(i)}))}, \ C(x) &:= \frac{\sum_{i}\mathsf k(x,x^{(i)})(x^{(i)}-m(x))\otimes(x^{(i)}-m(x))\exp(-\beta V(x^{(i)}))}{\sum_{i}\mathsf k(x,x^{(i)})\exp(-\beta V(x^{(i)}))}. \end{align}

Note that these weighted mean and covariance give more influence to particles which are close to $x$ and have a small value of $V$. If $\mathsf k(\cdot,\cdot)=1$ one recovers standard CBO and CBS.

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