Explained how to solve the convex subproblem from the CCSA (Svanberg, 2001) method (see lecture 29) using duality. We reduce it to a dual problem with only bound constraints on the dual variables, and then do CCSA recursively to obtain a dual problem with no variables at all (trivially solvable).

CCSA uses only the function value and gradient information from the current step, discarding the gradients from the previous steps; in that sense, it is similar in spirit to steepest-descent algorithms or successive LP approximations. More sophisticated algorithms, converging faster near the minimum, will also use (implicit or explicit) second-derivative information. This leads us to Newton and quasi-Newton methods.

Began discussing quasi-Newton methods in general, and the BFGS algorithm in particular. Motivated the problem: we want to construct a sequence of quadratic approximations for our objective function, and optimize these to obtain Newton steps in our parameters. Discussed backtracking line searches to ensure that the Newton step is a sufficient improvement. Discussed difficulty of using exact quadratic model (exact Hessian matrix) in general.

Discussed how quasi-Newton methods are used: they are used to generate not really a step (since the Newton step may be way off if the function is not near the minimum) but rather a direction for a line search. Discussed exact vs. approximate line searches (ala Nocedal, 1980).

Outlined four desirable properties of approximate Hessian matrix: positive definiteness, convergence to exact Hessian for quadratic objectives, multiplying it by the change in x should give the change in the gradient for the most recent step, and it should have as much "memory" as possible (minimizing the change to the Hessian or its inverse in some norm).