{
  "aviso": "speedup_declarado es lo que declara la fuente citada, NO una medición de Rosetta. Lo que Rosetta midió va en evidencia_rosetta, y para la mayoría del catálogo está vacío.",
  "procedencia": {
    "fuente": "Quantum Algorithm Zoo",
    "fuente_url": "https://quantumalgorithmzoo.org/",
    "instantanea_sha256": "dee7e76b5f19096ed329c88714744b93babf7b7d0296eb97e357b2582d16b75e",
    "generado_at": "2026-08-09"
  },
  "id": "convex-optimization",
  "nombre": "Convex Optimization",
  "categoria": "Optimization, Numerics, and Machine Learning",
  "categoria_id": "ONML",
  "problema": "Optimizar sobre cuerpos convexos y estimar sus volumenes, con acceso al cuerpo por oraculo de pertenencia.",
  "speedup_declarado": "Polynomial",
  "declarado_por": "Quantum Algorithm Zoo",
  "fuente_url": "https://quantumalgorithmzoo.org/#convex_optimization",
  "implementaciones": [],
  "referencias": [
    {
      "n": 130,
      "cita": "Martin Rötteler Quantum algorithms to solve the hidden shift problem for quadratics and for functions of large Gowers norm. In Proceedings of MFCS 2009 , pg 663-674. [ arXiv:0911.4724 ]",
      "url": "http://arxiv.org/abs/0911.4724"
    },
    {
      "n": 146,
      "cita": "Ashley Montanaro The quantum query complexity of learning multilinear polynomials. Information Processing Letters , 112(11):438-442, 2012. [ arXiv:1105.3310 ]",
      "url": "http://arxiv.org/abs/1105.3310"
    },
    {
      "n": 147,
      "cita": "Tad Hogg Highly structured searches with quantum computers. Physical Review Letters 80: 2473, 1998.",
      "url": null
    },
    {
      "n": 148,
      "cita": "Markus Hunziker and David A. Meyer Quantum algorithms for highly structured search problems. Quantum Information Processing , Vol. 1, No. 3, pg. 321-341, 2002.",
      "url": null
    },
    {
      "n": 223,
      "cita": "David A. Meyer and James Pommersheim Single-query learning from abelian and non-abelian Hamming distance oracles arXiv:0912.0583",
      "url": "http://arxiv.org/abs/0912.0583"
    },
    {
      "n": 418,
      "cita": "Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, and Xiaodi Wu Quantum algorithms and lower bounds for convex optimization arXiv:1809.01731",
      "url": "https://arxiv.org/abs/1809.01731"
    },
    {
      "n": 419,
      "cita": "S. Chakrabarti, A. M. Childs, S.-H. Hung, T. Li, C. Wang, and X. Wu Quantum algorithm for estimating volumes of convex bodies arXiv:1908.03903",
      "url": "https://arxiv.org/abs/1908.03903"
    },
    {
      "n": 420,
      "cita": "Joran van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf Convex optimization using quantum oracles arXiv:1809.00643",
      "url": "https://arxiv.org/abs/1809.00643"
    },
    {
      "n": 461,
      "cita": "Simon Apers and Sander Gribling Quantum speedups for linear programming via interior point methods arXiv:2311.03215 , 2023.",
      "url": "https://arxiv.org/abs/2311.03215"
    },
    {
      "n": 477,
      "cita": "Ankit Garg, Robin Kothari, Praneeth Netrapalli, and Suhail Sherif No quantum speedup over gradient descent for non-smooth convex optimization arXiv:2010.01801 , 2020.",
      "url": "https://arxiv.org/abs/2010.01801"
    },
    {
      "n": 497,
      "cita": "Yanlin Chen and Ronald de Wolf Quantum algorithms and lower bounds for linear regression with norm constraints arXiv:2110.13086 , 2021.",
      "url": "https://arxiv.org/abs/2110.13086"
    }
  ],
  "n_referencias": 11,
  "remisiones": [
    {
      "ancla": "gradients",
      "url": "https://quantumalgorithmzoo.org/#gradients"
    },
    {
      "ancla": "semidefinite",
      "url": "https://quantumalgorithmzoo.org/#semidefinite"
    }
  ],
  "evidencia_rosetta": {
    "medido": false,
    "lectura": "Rosetta no tiene ninguna corrida sellada sobre este algoritmo. Que esté catalogado no significa que lo hayamos medido ni que lo ofrezcamos."
  }
}