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Séminaire du GERAD
Gradient and Hessian Approximations for Model-based Blackbox Optimization
Warren Hare – Université de la Colombie-Britannique, Canada
A blackbox is a function that provides output without explanation of how the output was constructed. One common strategy for optimization involving blackbox functions is to numerically approximate gradients and/or Hessians and use these approximations in a classical method. In this talk, we examine the mathematical theory behind such an approach. We discuss classical and novel approximation techniques for blackbox functions. And, we illustrate the results on a case study from an application in Medical Physics.
Charles Audet
responsable
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Activité hybride au GERAD
Zoom et salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour
Montréal Québec H3T 1J4
Canada
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour
Montréal Québec H3T 1J4
Canada