Lecture 4.1: quadratic optimization: from optimality conditions to the gradient method
Completion requirements
Computing the "center" of the level sets of a quadratic non-homogeneous function: the less-than simple (singular) case. All in all: our first example of complete optimality conditions for a family of (simple) optimization problems. From the optimality condition to the gradient method: the important property (the anti-gradient is a descent direction). Theory breeds algorithms: the gradient method for quadratic functions, definition and discussion.