A reachability-analysis method on projected gradient descent dynamics produces certified outer approximations to the minimizer sets of strongly convex programs whose costs depend on bounded uncertain parameters.
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Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.
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Over-Approximating Minimizer Sets of Constrained Convex Programs with Parametric Uncertainty via Reachability Analysis
A reachability-analysis method on projected gradient descent dynamics produces certified outer approximations to the minimizer sets of strongly convex programs whose costs depend on bounded uncertain parameters.
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Distributionally Robust K-Means Clustering
Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.