Bayesian Optimization is a powerful strategy for optimizing expensive or unknown functions, utilizing a Gaussian Process (GP) to model the function and select promising points to sample by using Upper confidence bound acquisition function. This library provides a 1D GP-based Bayesian Optimization implementation for Arduino boards, including the ESP32 family.

• 1D Gaussian Process with an RBF (Radial Basis Function) kernel.
• Support for Bayesian Optimization using Upper Confidence Bound (UCB) acquisition.
• Easily configured hyperparameters:
  • Noise term
  • Length scale
  • Signal variance (denoted as sigma_f)
  • Exploration factor (denoted as alpha)
• Simple matrix inversion (Gauss-Jordan) for small datasets.
• Designed for microcontrollers like ESP32, ESP8266, or standard Arduino boards.
• Supports discrete scanning of a user-defined domain (e.g., [domainMin, domainMax] with increments).