pith:V7MX236T
Generalized Dual Decomposition
Generalized dual decomposition replaces the linear regularizer with a nonlinear one to restore strong duality while keeping parallel subproblem solves in two-stage mixed-integer stochastic programs.
arxiv:2605.14273 v1 · 2026-05-14 · math.OC
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\pithnumber{V7MX236TZ2S4HLFD5O2F77E7WQ}
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Record completeness
Claims
we propose a generalized dual decomposition (GDD) that extends the linear regularizer used in dual decomposition to a general nonlinear one, which still admits parallelization while exhibiting strong duality... we establish the convergence of a GDD algorithm to achieve global optimum.
That nonlinear regularizers can be encoded through parameterization and cutting planes in a way that preserves both parallelizability and strong duality without introducing post-hoc data-dependent choices that invalidate the global-optimality guarantee.
Generalized Dual Decomposition replaces linear regularizers with nonlinear ones in dual decomposition to achieve strong duality and global optimality for mixed-integer two-stage stochastic programs while retaining parallelization.
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Receipt and verification
| First computed | 2026-05-17T23:39:10.372901Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
afd97d6fd3cea5c3aca3ebb45ffc9fb41ea626c36a7fe48e881312a4daf31d8c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/V7MX236TZ2S4HLFD5O2F77E7WQ \
| 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: afd97d6fd3cea5c3aca3ebb45ffc9fb41ea626c36a7fe48e881312a4daf31d8c
Canonical record JSON
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