4 papers
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…
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices
Tetiana Parshakova, Trevor Hastie, Eric Darve +1
We consider multilevel low rank (MLR) matrices, defined as a row and column permutation of a sum of matrices, each one a block diagonal refinement of the previous one, with all blo…
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…
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…