Covers the computation of cost function for multivariable control systems using the LQR framework and applying gradient descent for controller improvement.
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Explores linear quadratic regulation for optimal control of linear systems, focusing on minimizing a quadratic cost function to move the system state towards zero.
Explores response theory, phase transitions, and fluctuations in weakly interacting systems, including stochastic particles and opinion formation models.
Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.