Blind Random Search with Noisy Loss Measurements: Averaging, Thresholding, and Almost Sure Convergence

Blind random search repeatedly draws a candidate point and replaces the current estimate whenever the candidate has a lower loss. In the absence of noise, the true loss is observed directly. It decreases strictly at every accepted update and is monotone nonincreasing over all iterations. Measurement