Lecture
Optimization methods
Related lectures (54)
Newton Method: Convergence and Quadratic Care
Covers the Newton method and its convergence properties near the optimal point.
Quasi-newton optimization
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Conjugate Gradient Optimization
Explores Conjugate Gradient optimization, covering quadratic and nonlinear cases, Wolfe conditions, BFGS, CG algorithms, and matrix symmetry.
Runge-Kutta Methods
Explains the Runge-Kutta methods, particularly the explicit scheme of order 4 (ERK4), and how to optimize parameters for accuracy.
Optimization Methods
Covers optimization methods without constraints, including gradient and line search in the quadratic case.
Optimization with Constraints: KKT Conditions
Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.
Optimization Methods
Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.
Optimization without Constraints: Gradient Method
Covers optimization without constraints using the gradient method to find the function's minimum.
Newton's Method: Optimization & Indefiniteness
Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.
Optimization with Constraints: KKT Conditions
Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.