Phase-dependent super-resolution bounds for two-point spectral estimation improve scaling in out-of-phase regimes and identify optimal algorithms across phase conditions.
Convex optimization: Algorithms and complexity,
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Symmetrizing Bregman divergences on positive definite matrices yields the arithmetic mean as canonical for forward symmetrization and the pulled-back dual arithmetic mean for reverse symmetrization, for any mirror map.
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Two-Point Resolution in Spectral Super-Resolution
Phase-dependent super-resolution bounds for two-point spectral estimation improve scaling in out-of-phase regimes and identify optimal algorithms across phase conditions.
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Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why
Symmetrizing Bregman divergences on positive definite matrices yields the arithmetic mean as canonical for forward symmetrization and the pulled-back dual arithmetic mean for reverse symmetrization, for any mirror map.