7 citations · 16 across the 6 of their papers we have counts for
8 papers · 1 filter
Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion
Brian Swenson, Ryan Murray, H. Vincent Poor +1
The paper considers distributed gradient flow (DGF) for multi-agent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often eas…
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
Brian Swenson, Soummya Kar, H. Vincent Poor +2
The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stoch…
On Distributed Stochastic Gradient Algorithms for Global Optimization
Brian Swenson, Anirudh Sridhar, H. Vincent Poor
The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorith…
Distributed Gradient Descent: Nonconvergence to Saddle Points and the Stable-Manifold Theorem
Brian Swenson, Ryan Murray, H. Vincent Poor +1
The paper studies a distributed gradient descent (DGD) process and considers the problem of showing that in nonconvex optimization problems, DGD typically converges to local minima…
Distributed Global Optimization by Annealing
Brian Swenson, Soummya Kar, H. Vincent Poor +1
The paper considers a distributed algorithm for global minimization of a nonconvex function. The algorithm is a first-order consensus + innovations type algorithm that incorporates…
Annealing for Distributed Global Optimization
Brian Swenson, Soummya Kar, H. Vincent Poor +1
The paper proves convergence to global optima for a class of distributed algorithms for nonconvex optimization in network-based multi-agent settings. Agents are permitted to commun…