Quadratic programming

He's an Avanessian professor in the IEOR department at Columbia University.

He is internationally recognized for the development and analysis of efficient and practical algorithms for solving various classes of optimization problems, including the BFGS quasi-Newton method for unconstrained optimization, steepest-edge simplex algorithms for linear programming, and the Goldfarb-Idnani algorithm for convex quadratic programming.

Don has also developed highly cited methods for image denoising, compressed sensing, matrix rank minimization, and robust portfolio selection.

In addition, he has developed simplex and combinatorial algorithms for network flow problems, interior point methods for linear quadratic and conic programming, and alternating linearization methods for some of convex functions.

He didn't come up with any of the ideas.

He was okay, but he wasn't a theoretician, certainly.

But we wrote that code, and I developed it, and I looked at quadratic programming as Not like just writing on the dual and then apply your standard algorithm to the dual.

I looked at a kind of a geometric thing.