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Two accepted local truncations can destroy positive definiteness after accumulation
Example
Let , , and . The exact sum is positive definite. If each rank-one update is separately recompressed to rank zero with tolerance before assembly, both local truncations are accepted and the assembled approximation is , which is indefinite. Each local error is below the smaller diagonal coefficient of . The example concerns truncation of low-rank updates before their sum is assembled; it does not claim that every SVD truncation of a completed positive-definite H-matrix destroys positivity.
Facts & Assumptions
Given: The matrices , tolerance , and rank cap for the two update compressions.
Core-SVD recompression with rank and error tolerances: the procedure accepts a rank-zero output when the discarded singular-value tail is at most .
Core-SVD recompression is best at the declared Frobenius tolerance: the returned rank-zero approximation has Frobenius error exactly its discarded singular-value tail.
Finite tensor arrays and the Frobenius inner product: the Frobenius norm is computed from the squared moduli of all matrix entries.
Verification
The exact sum is . For every nonzero , , so is positive definite.
Each has rank one and its only positive singular value is , so its rank-zero discarded tail and Frobenius error are both by [F3]. Thus [F1] accepts each update as zero, and [F2] gives its exact local error.
After both updates are replaced by zero, the sum is . Its quadratic form at is , so it is not positive definite. The accumulated perturbation is , whose action in this direction exceeds either local error; hence the two separate local tolerances do not preserve definiteness of the assembled sum.
Depends on
Used by
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Sources
- Ralf Hiptmair, AdvNumCSE Lecture Document, Chapter 2, “Local Low-Rank Compression of Non-Local Operators” (ETH Zürich, AT’23), §2.4.5 (standard reference, not scraped)