The Decentralized Trust-Region Method with Second-Order Approximations
This paper presents a novel decentralized trust-region framework that systematically incorporates second-order information to solve general nonlinear optimization problems in multi-agent networks. Our approach constructs local quadratic models that simultaneously capture objective curvature and enforce consensus through penalty terms, while supporting multiple Hessian approximation strategies including exact Hessians, limited-memory quasi-Newton methods, diagonal preconditioners, and matrix-free … Read more