We provide an explicit formula for the Levi-Civita connection and Riemannian Hessian for a Riemannian manifold that is a quotient of a manifold embedded in an inner product space with a non-constant metric function. Together with a classical formula for projection, this allows us to evaluate Riemannian gradient and Hessian for several families of metrics on classical manifolds, including a family of metrics on Stiefel manifolds connecting both the constant and canonical ambient metrics with closed-form geodesics. Using these formulas, we derive Riemannian optimization frameworks on quotients of Stiefel manifolds, including flag manifolds, and a new family of complete quotient metrics on the manifold of positive-semidefinite matrices of fixed rank, considered as a quotient of a product of Stiefel and positive-definite matrix manifold with affine-invariant metrics. The method is procedural, and in many instances, the Riemannian gradient and Hessian formulas could be derived by symbolic calculus. The method extends the list of potential metrics that could be used in manifold optimization and machine learning.
翻译:我们为利维塔-克利维塔连接和列伊曼尼埃西安海珊提供了里曼尼方块的明确公式,里曼尼方块是内产空间嵌入的元件的商数,具有非恒定的度量函数。这与经典的预测公式一道,使我们能够对里曼尼梯度和赫森等数系的古典元数组进行评估,包括一个Stiefel方块上的度量组,将常数和卡通环境度与闭式大地测量相连接。我们使用这些公式,将里曼尼方方方方块的优化框架取自斯特伊盖尔方块的商数,包括旗号元件,以及一套固定等级的正-西曼定基质矩阵的完全商数组。 这种方法是程序性的,在许多情形下,里曼梯度和赫西方方方方方方方方方块的公式可以通过象征性的计算公式来推算。 使用的方法可以扩展用于模型学习的模型。