Convex Geometry of the Generalized Matrix-Fractional Function
本文简化了广义矩阵分数函数的支撑函数表示及其次微分表示，使相关几何对象的计算变得容易，对逆问题、正则化和学习等领域的矩阵优化问题有用。
Generalized matrix-fractional (GMF) functions are a class of matrix support functions introduced by Burke and Hoheisel as a tool for unifying a range of seemingly divergent matrix optimization problems associated with inverse problems, regularization, and learning. In this paper we dramatically simplify the support function representation for GMF functions as well as the representation of their subdifferentials. These new representations allow the ready computation of a range of important related geometric objects whose formulations were previously unavailable.