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Distributionally robust chance-constrained standard quadratic optimization with Wasserstein ambiguity — arXiv:2610.39823

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39823
License (as archived): CC0-1.0
Archived: 2026-11-01T02:32:16.485Z
SHA-256 of the archived text: 9f2c53f83382a129176a1d9b3b7dbc3ce7b937677c31d9ab02c95412bd8b3ab9
Stable URL of this page: https://metraipolis.com/documento/dc_2bf419c65bb7

How to cite

Distributionally robust chance-constrained standard quadratic optimization with Wasserstein ambiguity — arXiv:2610.39823 — arXiv (en), https://arxiv.org/abs/2610.39823 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bf419c65bb7 (Metraipolis archive dc_2bf419c65bb7, sha256 9f2c53f83382a129176a1d9b3b7dbc3ce7b937677c31d9ab02c95412bd8b3ab9)

BibTeX

@misc{dc2bf419c65bb7,
  title = {Distributionally robust chance-constrained standard quadratic optimization with Wasserstein ambiguity — arXiv:2610.39823},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39823},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bf419c65bb7; sha256 9f2c53f83382a129176a1d9b3b7dbc3ce7b937677c31d9ab02c95412bd8b3ab9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39823. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The standard quadratic optimization problem consists of minimizing a quadratic form over the standard simplex. It has highly relevant applications ranging from portfolio optimization to machine learning. However, if the quadratic form is neither convex nor concave, this problem is NP-hard. We study a distributionally robust chance-constrained variant whose ambiguity set is a Wasserstein ball around the sampled distribution and make four contributions. First, although the problem is nonconvex in the decision variable, we exploit its linear dependence on the uncertain matrix to reduce the problem to a mixed-integer quadratic optimization problem. Second, we show that a judicious choice of matrix norm---the entrywise maximum norm---makes this reformulation both exact and tractable, and we describe the induced worst-case distribution. Third, our formulation is, by construction, the minimization of the worst-case Value-at-Risk of the random quadratic form; this unifies it with existing frameworks for uncertain standard quadratic optimization problems, in particular under chance constraints, and we identify natural settings in which it collapses to a single deterministic instance. Finally, we report numerical experiments on positive-definite and indefinite instances which quantify how the model parameters drive the optimal value and solution time, and expose the computational cost of indefiniteness.

Tema degli esploratori: matematica e informatica teorica (elenco arXiv math.OC, nuovi lavori del giorno).
Categorie arXiv: Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2610.39823 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:14:28.325Z dalla pagina https://arxiv.org/list/math.OC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).