Introduction

As we said, it is desirable that the subproblems in which we minimize the majorization function are simple. One way to guarantee this is to try to find a convex quadratic majorizer. We mostly limit ourselves to convex quadratic majorizers because on concave ones have no minima and are of little use for algorithmic purposes. Of course on compact sets minimizers of concave quadratics do exist, and may be useful in some circumstances.

A quadratic majorizes at on if and for all . If we write it in the form then . For differentiable we have in addition and for twice-differentiable we have If we limit ourselves to convex quadratic majorizers, we must also have .

We mention some simple properties of quadratic majorizers on .

  1. If a quadratic majorizes a twice-differentiable convex function at , then is a convex quadratic. This follows from .

  2. If a concave quadratic majorizes a twice-differentiable function at , then is concave at . This follows from .

  3. Quadratic majorizers are not necessarily convex. In fact, they can even be concave. Take and .}

  4. For some functions quadratic majorizers may not exist. Suppose, for example, that is a cubic. If is quadratic and majorizes , then we must have . But is a cubic, and thus for at least one value of .

  5. Quadratic majorizers may exist almost everywhere, but not everywhere. Suppose, for example, that . Then has a quadratic majorizer at each except for . If we can use, following Heiser [1986], the arithmetic mean-geometric mean inequality in the form and find If majorizes at 0, then we must have for all , and thus for all . But for and , we have .