A sparse LP with sign constraints learns balanced signed graph Laplacians from sample covariance, and an ADMM solver makes each column update linear when observations are scarce.
Sampling Signals on Graphs: From Theory to Applications,
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Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
A sparse LP with sign constraints learns balanced signed graph Laplacians from sample covariance, and an ADMM solver makes each column update linear when observations are scarce.