论文标题
熵正则化的曲折几何形状
Toric Geometry of Entropic Regularization
论文作者
论文摘要
熵正则化是用于大规模线性编程的方法。从几何学上讲，一个从桦树点开始的可行多层曲面与鳞片变种的相交。我们将其与对数屏障方法（从分析中心开始）进行比较，并将其与相互线性空间进行比较。我们重新访问熵正规化以进行不平衡的最佳运输，并开发了最佳圆锥耦合的使用。我们计算了相关的感谢您的多样性的程度，并探索算法等迭代缩放。
Entropic regularization is a method for large-scale linear programming. Geometrically, one traces intersections of the feasible polytope with scaled toric varieties, starting at the Birch point. We compare this to log-barrier methods, with reciprocal linear spaces, starting at the analytic center. We revisit entropic regularization for unbalanced optimal transport, and we develop the use of optimal conic couplings. We compute the degree of the associated toric variety, and we explore algorithms like iterative scaling.
