Local Acceptance without Global Agreement: Convergence of Decentralized Trust-Region Methods
We study decentralized smooth nonconvex optimization with a lower-bounded average objective and independent local trust-region acceptance decisions. Successful local trials can increase the average objective, while rejection produces a zero accepted inverse block at a nonzero gradient tracker. Our main contribution is a convergence analysis that permits such genuine local rejections. The method combines gradient … Read more