3 papers
math.OC2025
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…
math.OC2024
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…
stat.ML2024
Fitting Multilevel Factor Models
Tetiana Parshakova, Trevor Hastie, Stephen Boyd
We examine a special case of the multilevel factor model, with covariance given by multilevel low rank (MLR) matrix~\cite{parshakova2023factor}. We develop a novel, fast implementa…