pith:EZZC5TMQ
Approximation of Functions: Optimal Sampling and Complexity
Finite function evaluations impose strict information-theoretic limits on approximation accuracy.
arxiv:2602.02066 v2 · 2026-02-02 · math.NA · cs.IT · cs.NA · math.IT
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\usepackage{pith}
\pithnumber{EZZC5TMQEVNFWD4IAWREBLSGIO}
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Record completeness
Claims
Many fundamental insights into the information-theoretic limits of function approximation from finite evaluations were obtained only recently.
The discussion presupposes standard settings from optimal recovery and information-based complexity without detailing the precise function classes or norms under consideration.
Survey of information-theoretic limits and optimal sampling for function approximation from finite evaluations, including nonlinear and adaptive measurements.
Formal links
Receipt and verification
| First computed | 2026-06-24T14:15:55.057913Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
26722ecd90255a5b0f8805a240ae46439e02e3d944adf53b2bd32c6890363403
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EZZC5TMQEVNFWD4IAWREBLSGIO \
| 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())"
# expect: 26722ecd90255a5b0f8805a240ae46439e02e3d944adf53b2bd32c6890363403
Canonical record JSON
{
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],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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"submitted_at": "2026-02-02T13:07:27Z",
"title_canon_sha256": "dd3beade83f50c6d680452c0a189e34a480227de1f6fccf1cd17b8b06c092f92"
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"source": {
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