# CLUSTER: Derivative-free optimization of smooth functions with parameter-change costs

arXiv: https://arxiv.org/abs/2606.20498 · HTML: https://mlsanity.com/paper/2606.20498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [Vision-Language Agents for Active Perception in Optics Laboratories](https://mlsanity.com/paper/2609.32918/) (2026)

## Abstract (arXiv)

We introduce the CLUSTER algorithm (coordinate-level update strategy for trust-region step evaluation refinement) for local derivative-free optimization problems where there is a cost to changing each parameter (or clusters of parameters). For example, this type of cost model is appropriate for optimizing robot-controlled laboratory experiments, in which a robot may incur a separate motion for each parameter cluster to be adjusted. We build off of a class of quadratic-interpolation optimization algorithms by Powell and Conn that are known to perform well for twice-differentiable objectives (e.g. low-noise experiments), and show that the CLUSTER variants improve performance on a variety of test problems (including an optics laboratory experiment) by around 50%, and greatly outperform common competing algorithms for laboratory optimization (Bayesian optimization and Nelder--Mead). We also adapt the convergence proof of the Conn algorithm to obtain a similar convergence guarantee for CLUSTER-Conn.