72 citations · 146 across the 14 of their papers we have counts for
18 papers
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…
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
Brian Swenson, Ryan Murray, H. Vincent Poor +1
In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points and converges to local minima in nonconvex problems. However, similar guarante…
Smooth Fictitious Play in Potential Games
Brian Swenson, H. Vincent Poor
The paper shows that smooth fictitious play converges to a neighborhood of a pure-strategy Nash equilibrium with probability 1 in almost all (-player, two-action) po…
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…