Covers the Conjugate Gradient method for solving linear systems without pre-conditioning, exploring parallel computing implementations and performance predictions.
Introduces iterative methods for linear equations, convergence criteria, gradient of quadratic forms, and classical force fields in complex atomistic systems.
Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.
