CLUSTER: Derivative-free optimization of smooth functions with parameter-change costs
Speeding up lab experiments when moving between settings costs time and money
A new algorithm called CLUSTER optimizes laboratory experiments about 50% faster than existing methods when there's a penalty for adjusting each parameter or group of parameters—such as when a robot must physically reposition equipment. The approach works especially well for real-world lab setups like optics experiments, and outperforms popular alternatives like Bayesian optimization.
Robot-controlled labs waste time and resources repositioning equipment between every tiny parameter adjustment. CLUSTER reduces this waste by being smarter about which parameters to change together, cutting experiment time significantly. For labs running hundreds of optimization experiments—from drug discovery to materials science—this 50% speedup translates directly to faster results and lower costs.