5 citations · 13 across the 11 of their papers we have counts for
9 papers · 1 filter
A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow
Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu +3
A decomposition algorithm for solving large-scale security-constrained AC optimal power flow problems is presented. The formulation considered is the one used in the ARPA-E Grid Op…
On the Convergence of NEAR-DGD for Nonconvex Optimization with Second Order Guarantees
Charikleia Iakovidou, Ermin Wei
We consider the setting where the nodes of an undirected, connected network collaborate to solve a shared objective modeled as the sum of smooth functions. We assume that each summ…
S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication
Charikleia Iakovidou, Ermin Wei
We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gr…
A Two-Stage Decomposition Approach for AC Optimal Power Flow
Shenyinying Tu, Andreas Waechter, Ermin Wei
The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-sc…
FlexPD: A Flexible Framework Of First-Order Primal-Dual Algorithms for Distributed Optimization
Fatemeh Mansoori, Ermin Wei
In this paper, we study the problem of minimizing a sum of convex objective functions, which are locally available to agents in a network. Distributed optimization algorithms make…
Nested Distributed Gradient Methods with Stochastic Computation Errors
Charikleia Iakovidou, Ermin Wei
In this work, we consider the problem of a network of agents collectively minimizing a sum of convex functions. The agents in our setting can only access their local objective func…