Recovering Lower-Dimensional Semialgebraic Support of a Measure from its Moments
Recovering probability measures from their moments has numerous applications, esp. in connection with the method of moments in statistics and optimization. In the setting where measure need not be finitely atomic, but its support is known to be compact and semialgebraic with codimension at least one, the problem is still open. We combine moment-matrix kernel information with the Christoffel--Darboux kernel to provide a discrete approximation of the support. To validate the proposed approach, we test our algorithm on analytically computed moments and pseudo-moments arising from polynomial optimization problems without unique global minimizers. This complements well-known recent work on rec...
| Authors | Ruben Karapetyan, Shenyuan Ma, Ales Wodecki et al. |
|---|---|
| Primary category | math.ST |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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