4 papers
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers
April Sagan, John E. Mitchell
This paper considers a large class of problems where we seek to recover a low rank matrix and/or sparse vector from some set of measurements. While methods based on convex relaxati…
Low-Rank Factorization for Rank Minimization with Nonconvex Regularizers
April Sagan, John E. Mitchell
Rank minimization is of interest in machine learning applications such as recommender systems and robust principal component analysis. Minimizing the convex relaxation to the rank…
Decentralized Low-Rank State Estimation for Power Distribution Systems
April Sagan, Yajing Liu, Andrey Bernstein
This paper considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix com…
Matrix Completion Using Alternating Minimization for Distribution System State Estimation
Yajing Liu, April Sagan, Andrey Bernstein +3
This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method whi…