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Prior Information In Stochastic Optimization: Quasigradient Methods

Author

Listed:
  • Francisco Venegas-Martínez

    (Tecnológico de Monterrey, Campus Ciudad de México)

  • Gilberto Pérez-Lechuga

    (Tecnológico de Monterrey, Campus Ciudad de México)

Abstract

En este trabajo, se extiende el método de cuasi-gradiente estocástico cuando hay información a priori sobre la región en donde es probable encontrar direcciones descendentes. Nuestra extensión utiliza los estimadores de subgradiente de máxima entropía y de mínima entropía cruzada que incorporan la información a priori en la forma de valores esperados. Asimismo, analizamos varios patrones información a príori, y proporcionamos las condiciones de la convergencia para el método propuesto. Por último, obtenemos una representación de la distribución límite para la información esperada, la cual es proporcionada por una sucesión de estimadores de los subgradientes generados por el método propuesto.

Suggested Citation

  • Francisco Venegas-Martínez & Gilberto Pérez-Lechuga, 2003. "Prior Information In Stochastic Optimization: Quasigradient Methods," Remef - The Mexican Journal of Economics and Finance, Instituto Mexicano de Ejecutivos de Finanzas. Remef, vol. 2(2), pages 175-192, Junio 200.
  • Handle: RePEc:imx:journl:v:2:y:2003:i:2:p:175-192
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    File URL: http://www.remef.org.mx/index.php/primera/article/view/151
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    Keywords

    Stochastic quasigradient methods; Information theory;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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