Gradient and Hessian Approximations for Model-based Blackbox Optimization
Explore gradient and Hessian approximations for model-based blackbox optimization in this 48-minute seminar from GERAD Research Center. Delve into the mathematical theory behind optimizing functions that provide output without explanation. Examine classical and novel approximation techniques for blackbox functions, and see their application in a Medical Physics case study. Learn about solid state tank design optimization, Order-N accuracy, Newton's Method, and various gradient models. Investigate generalized simplex gradients, pseudo inverses, error bounds, and centered simplex gradients. Discover adjusted gradient techniques, simplex Hessians, and potential future research directions in this comprehensive talk by Warren Hare from the University of British Columbia.