Spectral embedding from the linearized synchronization dynamics matrix yields a natural decomposition of low-inertia power networks into coherent subsystems for distributed control.
Scalable Constrained Clustering: A Generalized Spectral Method
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We present a simple spectral approach to the well-studied constrained clustering problem. It captures constrained clustering as a generalized eigenvalue problem with graph Laplacians. The algorithm works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outperforms existing spectral approaches both in speed and quality.
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Generalized Spectral Clustering of Low-Inertia Power Networks
Spectral embedding from the linearized synchronization dynamics matrix yields a natural decomposition of low-inertia power networks into coherent subsystems for distributed control.