pith:4UZ2QYZN
Stochastic Convergence Analysis for Large-Scale Linear Discrete Ill-posed Problems
Derives stochastic error bounds and parameter-choice rules for weighted Tikhonov regularization of large-scale ill-posed problems with random noise under a polynomial eigenvalue assumption.
arxiv:2605.18259 v1 · 2026-05-18 · math.NA · cs.NA
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Claims
Under a polynomial upper-bound assumption on the generalized eigenvalues of the discrete forward operator, stochastic error bounds are derived for weighted Tikhonov regularization under independent zero-mean bounded-variance noise (expectation bounds) and independent sub-Gaussian noise (high-probability bounds), yielding an a priori parameter-choice rule and an adaptive strategy.
The polynomial upper-bound assumption on the generalized eigenvalues of the discrete forward operator (invoked to obtain the stochastic error bounds and parameter rule).
Derives stochastic error bounds and parameter-choice rules for weighted Tikhonov regularization of large-scale ill-posed problems with random noise under a polynomial eigenvalue assumption.
Receipt and verification
| First computed | 2026-05-20T00:05:52.480587Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e533a8632d1acd7c7de9a852c6be5f2c4fbf7650745f488238f07455f86239fc
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/4UZ2QYZNDLGXY7PJVBJMNPS7FR \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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