Sensitivity of primal-dual solutions in graph-induced NLPs decays exponentially with graph distance under SOSC and LICQ.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.
citing papers explorer
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Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
Sensitivity of primal-dual solutions in graph-induced NLPs decays exponentially with graph distance under SOSC and LICQ.
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Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.