Lectures related to Newton's Method: Solving Optimality Conditions

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Explores the design and optimization of car suspensions for improved performance.

Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.

Explores the transition from Rhino to AutoCAD, focusing on tools and features.

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Covers the derivation and application of Euler-Lagrange equations for optimization problems in mathematical analysis.

Explores practical aspects of Riemannian trust-region optimization and introduces the truncated conjugate gradient method.

Explores truncated conjugate gradients for solving the trust-region subproblem in optimization on manifolds efficiently.