SCIENCE CHINA Information Sciences, Volume 63 , Issue 5 : 152201(2020) https://doi.org/10.1007/s11432-019-9911-5

Application of gradient descent algorithms based on geodesic distances

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  • ReceivedApr 20, 2019
  • AcceptedMay 30, 2019
  • PublishedMar 26, 2020


In this paper, the Riemannian gradient algorithm and the natural gradient algorithm are applied to solve descent direction problems on the manifold of positive definite Hermitian matrices, where the geodesic distance is considered as the objective function. The first proposed problem is the control for positive definite Hermitian matrix systems whose outputs only depend on their inputs. The geodesic distance is adopted as the difference of the output matrix and the target matrix. The controller to adjust the input is obtained such that the output matrix is as close as possible to the target matrix. We show the trajectory of the control input on the manifold using the Riemannian gradient algorithm. The second application is to compute the Karcher mean of a finite set of given Toeplitz positive definite Hermitian matrices, which is defined as the minimizer of the sum of geodesic distances. To obtain more efficient iterative algorithm than traditional ones, a natural gradient algorithm is proposed to compute the Karcher mean. Illustrative simulations are provided to show the computational behavior of the proposed algorithms.


Xiaomin DUAN was supported by National Science and Technology Major Project of China (Grant No. 2016YFF02030012), National Natural Science Foundation of China (Grant No. 61401058) and Natural Science Foundation of Liaoning Province (Grant No. 20180550112). Huafei SUN was partially supported by National Natural Science Foundation of China (Grant No. 61179031). Linyu PENG was supported by JSPS Grant-in-Aid for Scientific Research (Grant No. 16KT0024), the MEXT “Top Global University Project", Waseda University Grant for Special Research Projects (Grant Nos. 2019C–179, 2019E–036), and Waseda University Grant Program for Promotion of International Joint Research.


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  • Figure 1

    (Color online) Positive definite Hermitian matrix system.

  • Figure 11

    (Color online) Comparison of two algorithms.


    Algorithm 1 Riemannian gradient descent algorithm for positive definite Hermitian matrix systems

    1. Set $u_0=(u_0^1,~u_0^2,\ldots~,u_0^m)$ as an initial input. Choose a fixed learning rate $\eta$ for simplicity and a desired tolerance $\varepsilon>0$.

    2. At time $k$, calculate $A(u_k)$ using Theorem 1 and $d({A(u_k)},B)$.

    3. If $d({A(u_k)},B)<\varepsilon$ then stop. Otherwise, move to step $4$.

    4. Make $k$ plus one, and then run step $2$.


    Algorithm 2 Natural gradient algorithm for positive definite Hermitian matrix systems

    1. Set $u_0=(u_0^1,~u_0^2,\ldots~,u_0^m)$ as an initial input. Choose a fixed learning rate $\eta$ and a desired tolerance $\varepsilon>0$.

    2. At time $k$, calculate $u_k$ using Theorem 2 and ${\nabla}J(u_k)$.

    3. If $\|{\nabla}J(u_k)\|_F<\varepsilon$, stop. Otherwise, move to step $4$.

    4. Make $k$ plus one, and then run step $2$.


    Algorithm 3 Natural gradient algorithm for the Karcher mean of $N$ matrices on manifold ${\rm~Sym}(n,\mathbb{C})$

    1. Take the arithmetic mean $\frac{1}{N}\sum_{i=1}^NR^i$ as the initial point $\theta_0$. Choose a learning rate $\eta$ and a desired tolerance $\varepsilon>0$.

    2. At time $k$, calculate $\theta_k$ using Theorem 3 and ${\nabla}L(\theta_k)$.

    3. If $\|{\nabla}L(\theta_k)\|<\varepsilon$, stop. Otherwise, move to step $4$.

    4. Make $k$ plus one, and then run step $2$.

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