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pith:5MGIFMV4
pith:2026:5MGIFMV4A5MN7SDWCBLB3SEED2
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Convergence Rates for $\ell_p$ Norm Minimization in Convex Vector Optimization
The Hausdorff approximation error converges at rate O(k^{2/(1-q)}) for every ℓ_p norm in convex vector optimization.
arxiv:2605.14324 v1 · 2026-05-14 · math.OC · cs.NA · math.NA
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Claims
C1strongest claim
We prove that the Hausdorff approximation error satisfies δ_H(P_k, A) = O(k^{2/(1-q)}) for every p ∈ (1,∞)
C2weakest assumption
The technique assumes the ambient space is R^q with its standard inner product structure, which enables the quadratic bound on hyperplane distance; this may not extend directly to non-Euclidean settings or infinite dimensions.
C3one line summary
The Hausdorff error for ℓ_p-norm based outer approximations in convex vector optimization converges at the optimal rate O(k^{2/(1-q)}) independently of p.
References
[1] A Norm Minimization-Based Convex Vector Optimization Algorithm,
[2] Convergence Analysis of a Norm Minimization-Based Convex Vector Optimization Algorithm,
[3] Asymptotic estimates for best and stepwise approximation of convex bodies II,
[4] Asymptotic estimates for best and stepwise approximation of convex bodies I,
[5] Asymptotic estimates for best and stepwise approximation of convex bodies III,
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| First computed | 2026-05-17T23:39:09.792220Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
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eb0c82b2bc0758dfc87610561dc8841eaeac31f44ec5518ef4840f4bc427c951
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/5MGIFMV4A5MN7SDWCBLB3SEED2 \
| 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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