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pith:JNJRFGSW
pith:2026:JNJRFGSWXFBAMSEKCOVJAA4M5D
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The Method of Ellipcenters for strongly convex minimization
The Method of Ellipcenters converges linearly for any differentiable strongly convex objective.
arxiv:2605.12820 v1 · 2026-05-12 · math.OC
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Claims
C1strongest claim
we derive convergence for any differentiable strongly convex objective
C2weakest assumption
That ellipses can be constructed at each step to capture the ill-conditioning of an arbitrary differentiable strongly convex function while preserving the linear rate.
C3one line summary
ME achieves linear convergence for any differentiable strongly convex objective by centering iterates inside carefully chosen ellipses.
References
[1] Introducing the method of ellipcenters, a new first order technique for unconstrained optimization.arXiv, 2025
[2] Two-Point Step Size Gradient Methods.IMA Journal of Numerical Analysis, 8(1):141–148, 1988
[3] IMAGING SCIENCES, 2 (1):183-202, 2009
[4] M´ ethode g´ en´ erale pour la r´ esolution des syst` emes d’´ equations simultan´ ees
[5] Practical Methods of Optimization (2nd ed.)New York: John Wiley & Son, 1987
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| First computed | 2026-05-18T03:09:12.228237Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4b53129a56b94206488a13aa90038ce8c716dbd5680dc77f95aa4a88d69479e5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/JNJRFGSWXFBAMSEKCOVJAA4M5D \
| 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: 4b53129a56b94206488a13aa90038ce8c716dbd5680dc77f95aa4a88d69479e5
Canonical record JSON
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