6 citations · 6 across the 9 of their papers we have counts for
3 papers · 1 filter
Multiple Approximate-Response Agents (MARA): Fast Near-Optimal Primal Recovery for Distributed Optimization
Tetiana Parshakova, Yicheng Bai, Garrett van Ryzin +1
Dual methods are useful for distributed optimization because they allow agent-level subproblems to be solved in parallel. However, achieving primal feasibility with dual methods is…
Optimization Algorithm Design via Electric Circuits
Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu +1
We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is…
Implementation of an Oracle-Structured Bundle Method for Distributed Optimization
Tetiana Parshakova, Fangzhao Zhang, Stephen Boyd
We consider the problem of minimizing a function that is a sum of convex agent functions plus a convex common public function that couples them. The agent functions can only be acc…