Healthcare workers: diminishing burnout symptoms through self-care

1.   Introduction

We consider the numerical solution of the following system of nonlinear equations

where $F:\mathbb{R}^n\rightarrow \mathbb{R}^m$ is a continuously differentiable function.

Nonlinear equations of the form (1.1) are often solved as a key ingredient in simulations of many real-world problems. Classic methods for solving (1.1) include the Gauss-Newton method, the inexact Newton method, the Broyden's method and the trust region method ^[9,13,15]. In actual computations, however, the Gauss-Newton method becomes less competitive when the Jacobian is (nearly) rank-deficient. By adopting a trust-region approach in place of the line search in the Gauss-Newton method, the Levenberg-Marquardt (LM) method circumvents this shortcoming even though it uses the same Hessian approximations as in the Gauss-Newton method.

In the trial step of the LM method, one needs to solve per step the following linear system

where $\lambda_k\geq0$, $F_k = F(x_k)$, $J_k = J(x_k)$ is the Jacobian and $I\in\mathbb{R}^{n\times n}$ stands for the identity matrix. If $J_k$ is nonsingular and Lipschitz continuous for the case $m = n$, the initial guess $x_0$ is close enough to the solution $x^*$ of (1.1) and the LM parameter $\lambda_k$ is updated recursively, then the LM method has a quadratic convergence rate.

For some applications, the need for a nonsingular Jacobian $J_k$ can be rather stringent. Therefore, it is necessary to come up with numerical methods in the absence of a nonsingular Jacobian. To this end, some efforts have been made recently; for instance, Yamashita et al. propose a local error bound condition which does not requires nonsingularity of the Jacobian ^[19]. In what follows, we denote by $X^*$ the nonempty solution set of (1.1) and use $\|\cdot\|$ to represent the 2-norm of vectors or matrices if there is no ambiguity. Let $N(x^*, b) = \{x\; |\; \|x-x^*\|\leq b\}$ be a subset of the $n$-dimensional vector space such that the intersection $X^*\cap {N(x^*, b)}$ is nonempty. The LM method is shown to have a quadratic convergence rate if there exists a positive constant $c$ satisfying the following local error bound condition ^[2,6,19]

where $\mathrm{dist}(x, X^*)$ is the distance from $x$ to $X^*$.

In spite of the advantage of avoiding nonsingularity of the Jacobian, the local error bound condition (1.3) is not always applicable for some ill-conditioned nonlinear equations from application fields like biochemical systems. In light of this, Guo et al. present the Hölderian error bound condition that is more applicable than (1.3) ^[8]. The {Hölderian error bound condition} is given by

where $c > 0$ and $\gamma\in(0, 1]$. Obviously, the Hölderian error bound condition (1.4) includes the local error bound condition (1.3) as a special case. In fact, the bound (1.4) reduces to (1.3) when $\gamma = 1$. It should be noted that the Hölderian local error bound condition is also called Hölder metric subregularity which is closely related with the Łojasiewica inequalities; see ^[14,17] for detail. With the assumption (1.4), the LM method converges at least superlinearly when $\gamma$ and the LM parameter satisfy certain conditions ^[1,8,18,21].

Apart from its application in solving systems of nonlinear equations, the LM method also finds its way into numerical solution of nonlinear least squares problems. To investigate the local convergence of the LM method for the nonlinear least-squares problem with possible nonzero residue, Behling et al. ^[3] present a local error bound condition characterized by $\|J(x)^TF(x)\|$, i.e.,

where $c > 0$. We stress that the local error bound condition (1.5) can also be derived from the bound (1.3) [10,Lemma 5.4]. However, the former is more practical than the latter in that it does not require the nonsingularity of the Jacobian. With the assumption (1.5), the LM method is shown to have at least linearly convergence order with suitable choices of the LM parameter ^[3].

As observed from (1.2), the LM parameter $\lambda_k$ is introduced in case that $J_k^TJ_k$ is (nearly) singular. Such practice not only guarantees the uniqueness of solution of (1.2) but also helps to reduce the iteration steps. In this sense, the LM parameter plays a key role in the LM method. Some promising candidates of the LM parameter have been proposed recently; for instance, Yamashita et al. ^[19] select $\lambda_k = \|F_k\|^2$ and show that the LM method has quadratically convergence with the assumption (1.3). Fan and Yuan ^[6] generalize it with the LM parameter $\lambda_k = \|F_k\|^{\delta}$ with $\delta\in[1,2]$. It is shown that the quadratic convergence is still retained with the assumption (1.3). Dennis and Schnable consider the choice $\lambda_k = O(\|J_k^TF_k\|)$ ^[4]. Following this reasoning, Fischer employs $\lambda_k = \|J_k^TF_k\|$ in ^[7] which is further generalized to the form $\lambda_k = \|J_k^TF_k\|^\delta$ with $\delta\in(0, 1]$ in ^[3]. With the assumtion (1.5), Behling et al. conclude that the LM method converges at least linearly to some solution of (1.1) when $\delta\in(0, 1)$ and quadratically when $\delta = 1$ ^[3]. More recent progress in choosing the LM parameter $\lambda_k$ can be found in ^[5,10,11].

Instead of adopting the choice used in ^[3], we propose to use the LM parameter $\lambda_k = \|J_k^TF_k\|^\delta$ with $\delta\in[1,2]$ in this work. The motivation of our work is clarified as follows. Intuitively, the step size $\|d_k\|$ is small if $\|J_k^TF_k\|$ is too large, which may hamper a fast convergence. Fortunately, it poses no difficulty by considering the following choice, i.e.,

From the convergence theory, we know $\|J_k^TF_k\|$ always converges to $0$, hence $\|J_k^TF_k\| > 1$ only occurs at beginning finite iterate steps and it is a special case for the numerical method. Since the choice of $\lambda_k$ in (1.6) is adaptive, then the variant LM method is called an adaptive Levenberg-Marquardt method (ALMM) in this paper.

The rest of this paper is organized as follows. In Section 2, the adaptive Levenberg-Marquardt method is introduced. Its convergence rate under the assumption (1.5) is examined. In section 3, the adaptive Levenberg-Marquardt method with Wolfe line search rule as well as its global convergence are investigated. In Section 4, some numerical experiments are used to verify the effectiveness of the new method. Finally, some conclusions are given in Section 5.

2.   Local Convergence of the adaptive LM method

In this section, we consider the adaptive LM method with unit step size and investigate its local convergence near a solution.

To begin with our discussion, we present the following adaptive LM method:

where the LM parameter is defined in (1.6).

To establish the local convergence results for the adaptive LM algorithm, we need the following assumptions throughout the paper.

Assumption 2.1. (a) The Jacobian $J(x)$ is Lipschitz continuous in a neighborhood $N(x^*, b)$, i.e., there exists a constant $L_1 > 0$ such that

(b) We said that $\|J(x)^TF(x)\|$ provides a local error bound on $N(x^*, b)$ if there exists a constant $c > 0$ such that

To guarantee the initial point $x_0$ is sufficiently close to $x^*$, we assume $b > 0$ is sufficient small.

From Assumption 2.1(a), we note that

By compactness, we have

where constants $L_2 > 0$ and $\beta > 0$. Therefore, it follows from the mean value inequality that

Denote by $\bar{x}_k\in X^*$ which satisfies

Lemma 2.1. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. There exists some positive constants $c_1, \tilde{c}_1,$ such that

Proof. We derive the proof in two cases.

Case I: $\|J_k^TF_k\|\leq1$. Then $\lambda_k = \|J_k^TF_k\|^{\delta}$. From Assumption 2.1 (b), the inequality in the left-hand side (2.7) is obtained, i.e.,

Now, we verify the right-hand side inequality in (2.7).

It follows from (2.5) and (2.6) that

where $c_1 = L_2^2+\beta L_1$. Since $\lambda_k = \|J_k^TF_k\|^{\delta}$, then we obtain

Case II: $\|J_k^TF_k\| > 1$. Then $\lambda_k = \|J_k^TF_k\|^{-\delta} < 1$. From (2.8), we also have

Summarizing the above two cases, we obtain the inequality (2.7) with $\tilde{c}_1 = \min\{c^{\delta}, c_1^{-\delta}\}$. The proof is completed.

Lemma 2.2. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. If $x_k\in N(x^*, b/2)$, there exists a constant $c_2 > 0$ such that

{Proof. From the assumption, we have

which indicates that $\bar{x}_k\in N(x^*, b)$. Define

From (2.1) and the convexity of $\varphi_k(d)$, we note that $d_k$ is not only a stationary point but also a minimizer of $\varphi_k(d)$. By using the fact that $x_k, \bar{x}_k\in N(x^*, b)$, we have from (2.4) and Lemma 2.1 that

It implies that

where $c_2 = \sqrt{L_1^2\tilde{c}_1+1}$. The proof is completed.

Lemma 2.3. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. Assume $x_k, x_{k+1}\in N(x^*, b/2)$, then

Proof. For all $x_k, x_{k+1}\in N(x^*, b/2)$, we get from (2.4) and (2.5) that

and

By the triangle inequality, the above inequality yields

For all $\bar{x}_k\in X^*\cap N(x^*, b)$, we obtain

Similarly, using the triangle inequality yields

It follows from (1.2), (2.11) and (2.13) that

From Lemma 2.2, we have $\|d_k\|\leq c_2\|\bar{x}_k-x_k\|$, which implies that

Since $\bar{x}_k\in X^*\cap N(x^*, b)$ and $\delta\in[1,2]$, together with Assumption 2.1 (b), (2.12), (2.14) and (2.15), we obtain

The proof is completed.

Henceforth, according to the choices of the LM parameter, namely $\|J_k^TF_k\|\leq1$ and $\|J_k^TF_k\| > 1$, we divide the convergence analysis in two cases.

Case 1: $\|J_k^TF_k\|\leq1$

Firstly, we consider the convergence rate of the adaptive LM method with the LM paramter $\|J_k^TF_k\|\leq1$ in this subsection.

Lemma 2.4. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. If $x_k, x_{k+1}\in N(x^*, b/2)$ and $\|J_k^TF_k\|\leq1$, then there exists a positive constant $c_3$ such that

Proof. From Lemmas 2.1 and 2.3, we have

Since $\delta\in[1,2]$, then Lemma 2.4 holds with $c_3 = c^{-1}(L_1L_2(2+3c_2+2c_2^2)+L_2c_2c_1^{\delta}+L_1L_2^2(2+c_2)(1+c_2)^2)$. The proof is completed.

Lemma 2.4 shows that if $x_k\in N(x^*, b/2)$ for all $k$, then $\{\mathrm{dist}(x_k, X^*)\}$ converges to zero quadratically. Next, we show that the latter theory holds if $x_0$ is sufficiently close to $x^*$. Let

Lemma 2.5. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. If $x_0\in N(x^*, r)$ with $r$ given by (2.17), then for all $k$, we have $x_k\in N(x^*, b/2)$.

Proof. We show the proof by induction. It follows from Lemma 2.2 that

It indicates that $x_1\in N(x^*, b/2)$. Assume for $i = 2, \cdots, k$, $x_i\in N(x^*, b/2)$. It follows from Lemma 2.4 that

where the last inequality is derived from $\|x_0-x^*\|\leq r$ and $r\leq{1}/{2c_3}$. Therefore, we have from Lemma 2.2

for $i = 1, \cdots, k$. It then follows from (2.17) that

which indicates that $x_{k+1}\in N(x^*, b/2)$. The proof is completed.

Theorem 2.1. Let Assumption 2.1 hold and $\{x_k\}$ be the LM sequence which is generated by the adaptive LM method with $x_0\in N(x^*, r)$, where $r$ is given by (2.17). If $\|J_k^TF_k\|\leq1$, then the sequence $\{\mathrm{dist}(x_k, X^*)\}$ converges to zero quadratically. Moreover, $\{x_k\}$ converges to a solution of (1.1).

Proof. Lemma 2.4 and 2.5 indicates that the sequence $\{\mathrm{dist}(x_k, X^*)\}$ converges to $0$ quadratically. So, we only have to prove the second part.

According to the assumption, we have $x_k\in N(x^*, b/2)$ for all $k$. Then we only have to prove that $\{x_k\}$ converges to some solution $\bar{x}\in X^*$. In fact, for any $p, q\in \mathbb{N}_+$ (let $p\geq q$, we also obtain the same result for $p < q$), from (2.18), we have

The above inequality indicates that the sequence $\{x_k\}$ is a Cauchy sequence, and hence $\{x_k\}$ converges. The proof is completed.

Theorem 2.1 shows that the sequence $\{\mathrm{dist}(x_k, X^*)\}$ converges to zero quadratically and $\{x_k\}$ converges to the solution set $X^*$. However, little is known about the behaviour of the sequence $\{x_k\}$. In the following theorem, we will see that the sequence $\{x_k\}$ converges to a solution $\bar{x}$ of (1.1), and that the rate of convergence is also locally quadratic.

Theorem 2.2. Let Assumption 2.1 hold, $\{x_k\}$ be the LM sequence which is generated by the adaptive LM method with $x_0\in N(x^*, r)$ where $r$ is given by (2.17), and limit point $\hat{x}^*\in X^*\cap N(x^*, b/2)$. If $\|J_k^TF_k\|\leq1$, then the sequence $\{x_k\}$ converges to $\hat{x}^*$ quadratically.

Proof. In view of Theorem 2.1, we have $\mathrm{dist}(x_{k+1}, X^*)\leq\frac{1}{2}\mathrm{dist}(x_k, X^*)$ for all sufficiently large $k$. By letting $p\rightarrow \infty$ in (2.19), we deduce from Lemma 2.2 and 2.4 that

where the last inequality follows from the definition of $\mathrm{dist}(x_k, X^*)$. Hence, the sequence $\{x_k\}$ converges to $\hat{x}^*$ quadratically. The proof is completed.

Case 2: $\|J_k^TF_k\| > 1$

Now, we consider the convergence rate of adaptive LM method with the LM paramter $\|J_k^TF_k\| > 1$.

Lemma 2.6. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. Assume $\|J_k^TF_k\| > 1$, if $x_k, x_{k+1}\in N(x^*, b/2)$ and $\mathrm{dist}(x_k, X^*)\leq r < 1$, where

with $c > L_2c_2$, then there exists a positive constant $c_4\in(0, 1)$ such that

Proof. From Lemma 2.1, we have $\lambda_k < 1$. Together with Lemma 2.3, we obtain

which indicates that Lemma 2.6 holds with $c_4 = c^{-1}(L_1L_2(2+3c_2+2c_2^2)+L_1L_2^2(2+c_2)(1+c_2)^2) r +c^{-1}L_2c_2$.

The proof is completed.

Lemma 2.7. Let the sequence $\{x_k\}$ be generate by the adaptive LM method and Assumptions 2.1 hold. If $x_0\in N(x^*, r)$ with $r$ given by (2.20), then for all $k$, we have $x_k\in N(x^*, b/2)$ and $\mathrm{dist}(x_k, X^*)\leq r$.

Proof. Since the proof is analogous to the one of Lemma 2.5, we only verify the inductive step, i.e., assume Lemma 2.7 holds with $i = k$ and consider the next step.

It follows from Lemma 2.6 that

and

Thus, from Lemma 2.2 and (2.20), we have

which indicates that $x_{k+1}\in N(x^*, b/2)$. The proof is completed.

Theorem 2.3. Let Assumption 2.1 hold and $\{x_k\}$ be the LM sequence which is generated by the adaptive LM method with $x_0\in N(x^*, r)$, where $r$ is given by (2.20). If $\|J_k^TF_k\| > 1$, then the sequence $\{\mathrm{dist}(x_k, X^*)\}$ converges to zero linearly. Moreover, the sequence $\{x_k\}$ converges to a solution $\hat{x}^*\in X^*\cap N(x^*, b/2)$ linearly.

Proof. The proof is similar to the proofs of Theorems 2.1 and 2.2.

3.   Global convergence of the adaptive LM method

To establish the global convergence of the adaptive LM method, we employ some line search rules such as Armijo rule, Goldstein rule and Wolfe rule ^[15] etc. Consider the merit function

At iteration $k$, the next step is computed by

where $d_k$ is a direction from (2.1) and $\alpha_k$ is a step size satisfying certain line search conditions. The Wolfe line search is one of commonly used inexact line search which requires $\alpha_k > 0$ satisfies

and

Here $\sigma_1\leq\sigma_2$ are two constants in $(0, 1)$.

Algorithm 3.1 (The adaptive LM method with Wolfe line search).

Step 1: Given $x_0\in\mathbb{R}^n$, $\delta\in[1,2]$, $\eta\in(0, 1)$, $\sigma_1\in(0, 1/2)$, $\sigma_2\in(\sigma_1, 1)$, $k: = 0$.

Step 2: If $\|J_k^TF_k\| = 0$, stop. Set $\lambda_k$ as (1.6); determine $d_k$ by computing (2.1).

Step 3: If $d_k$ satisfies

set $x_{k+1} = x_k+d_k$, and go to step 5. Otherwise, go to step 4.

Step 4: Set $x_{k+1} = x_k+\alpha_kd_k$, where $\alpha_k$ is determined by Wolfe line search.

Step 5: Set $k: = k+1$; go to Step 2.

Theorem 3.1. Assume $F(x)$ is continuously differentiable. Let $\{x_k\}$ be a sequence generated by Algorithm 3.1. Then any accumulation point $x^*$ of $\{x_k\}$ is a stationary point of $\Phi$.

Proof. From [20,Eq (2.10)], the inequality (3.1) implies that

where $\sigma_3$ is some positive constant. Together with Steps 3 of Algorithm 3.1, the sequence $\{\|f(x_k)\|\}$ is monotonically decreasing and bounded from below, and thus converges to zero. Hence $\{x_k\}$ converges to a stationary point $x^*$ of $\Phi$. The proof is completed.

Theorem 3.2. Under Assumption 2.1, let $\{x_k\}$ be a sequence generated by Algorithm 3.1 and has an accumulation point $x^*$. If $x^*$ is a solution of system of nonlinear Eq (1.1), then the sequence $\{x_k\}$ converges to $x^*$ at least linearly.

Proof. It is sufficient to show that $\|F(x_k+d_k)\|\leq\eta\|F(x_k)\|$ holds for all large $k$.

If $\|J_k^TF_k\|\leq1$. Since the sequence $\{x_k\}$ converges to a stationary point $x^*$ which is a solution of system of nonlinear Eq (1.1), we have that

and

hold for all sufficiently large $K\in\mathbb{N}$, where $r$ is defined by (2.17), and $c$, $c_3$ and $L_2$ are given in Section 2.

Let sequence $\{y_k\}$ be generated by the adaptive LM method with unit step size and $y_0 = x_K$. Then, by the result of Theorem 2.1, the sequence $\mathrm{dist}(y_l, X^*)$ quadratic converges to zero. Hence, we only have to prove that $x_{K+l} = y_l$ for all $l\in\mathbb{N}$, i.e., the sequence $\{y_l\}$ satisfies

Let $\bar{y}_{l+1}\in X^*$ such that $\mathrm{dist}(y_{l+1}, X^*) = \|\bar{y}_{l+1}-y_{l+1}\|$. Then we obtain from Assumption 2.1(b), Lemma 2.4, (2.6) and (3.4) that

holds for $\eta\in(0, 1)$ and all $l$. The above inequality indicates that the step size $\alpha_k = 1$ holds for all large $k$ in Algorithm 3.1. We conclude that (3.2) holds for all $k\geq K$. Consequently, by mathematical induction, Algorithm 3.1 reduces to the adaptive LM method for all $k\geq K$. Thus, we have that $\{x_k\}$ converges to the solution $x^*$ quadratically.

Similar to the above process, when $\|J_k^TF_k\| > 1$, we obtain that $\{x_k\}$ converges to the solution $x^*$ linearly.

The proof is completed.

4.   Numerical examples

In this section, we carry out some numerical experiments to verify the effectiveness of the proposed adaptive Levenberg-Marquardt method (ALMM). The Levenberg-Marquardt method (LMM) given by Behling et al. ^[3] is used for comparison. The first test is a nonlinear least squares problem while the second are some systems of nonlinear equations.

Example 4.1. Consider the nonlinear least squares problem ^[3]

where $F(x) = (x_1^3-x_1x_2+1, x_1^3+x_1x_2+1)^T$.

Consider $X^* = \{(0, \xi), \xi\in\mathbb{R}\}$ be the non-isolated set of minimizers such that $\mathrm{dist}(x, X^*) = |x_1|$. Then the rank of the Jacobian will be $0$ at the origin, $1$ at $x$ with $x_1 = 0$ for $x_2\neq0$, and $2$ for $x_1\neq0$. Thus the Jacobian is not always of full rank at the stationary points. The starting point is set to be $x_0 = (0.008, 2)^T$. All methods terminate if $\|J_k^TF_k\| < 10^{-10}$. The results are tabulated in Table 1.

As illustrated, ALMM generally converges to the required accuracy with less iterations than LMM. Besides, distances between $x_k$ obtained from ALMM and the solution set $X^*$ are shorter than those from LMM.

Example 4.2. Consider systems of nonlinear equations adapted from the nonsingular problems given in ^[12,16]

where $F(x)$ is the standard nonsingular test function, $x^*$ is its root, and $A\in\mathbb{R}^{n\times k}$ has full column rank with $1\leq k\leq n$. It is easy to check that $\hat{F}(x^*) = 0$ and the rank of $\hat{J}(x^*) = J(x^*)(I-A(A^TA)^{-1}A^T)$ is $n-k$. A disadvantage of these problems is that $\hat{F}(x)$ may have roots that are not roots of $F(x)$. We present two sets of singular problems with the rank of $\hat{J}(x^*)$ being $n-1$ and $n-2$, respectively. The corresponding matrices of $A$ and $A^T$ are given by

and

Note that the size of the original problem which has $n+2$ equations in $n$ unknowns is reduced by eliminating the $(n-1)$st and the $n$th equations.

Several choices of the LM parameter are considered in the two LM methods. In accordance with the range of $\delta$ defined in LMM and ALMM, we use $\delta = 10^{-4}$, $0.5$ and $1$ associated with $\lambda_k = \|J_k^TF_k\|^\delta$ for LMM and employ $\delta = 1$, $1.5$ and $2$ for ALMM. All algorithms are terminated if $\|J^T_kF_k\| < 10^{-6}$ or the number of the iterations exceeds $100(n+1)$. Numerical results for the rank $n-1$ case and the rank $n-2$ case are listed in Table 2 and in Table 3, respectively. The values 1, 10 and 100 in the third column associate with starting points with $x_0$, $10x_0$, and $100x_0$, where $x_0$ is the option suggested in ^[12]. The symbol "–" is used if the corresponding method fails to reach the required accuracy within the prescribed maximum iterations. To ensure the numerical stability, we use the MATLAB function $\texttt{pcg}$ (the preconditioned conjugate gradient method) to solve the inner linear system (1.2).

Some remarks are in order. In all tests, ALMM converges to the required accuracy within the maximum iterations while LMM fails for some cases; see, for instance, Powell badly scaled problem in Table 2 and Discrete integral equation problem in Table 3. Furthermore, the number of iteration step required by ALMM is less than that by LMM. For this reason, we conclude that ALMM is a competitive variant of the Levenberg-Marquardt method.

5.   Conclusions

We present a Levenberg-Marquardt method with an adaptive LM parameter for solving systems of nonlinear equations. We have analyzed its local and global convergence under a new error bound condition of function, which can be derived from the local error bound condition, and Lipschitz continuity of the Jacobian. These properties hold in many applied problems, as they are satisfied by any real analytic function. The effectiveness of the adaptive Levenberg-Marquardt method is validated by the numerical examples.

Acknowledgements

The work of Lin Zheng was supported by the Natural Science Foundation of the Higher Education Institutions of Anhui Province grant KJ2020A0017. The work of Liang Chen was supported by the Abroad Visiting of Excellent Young Talents in Universities of Anhui Province grant GXGWFX2019022 and the Natural Science Foundation of the Higher Education Institutions of Anhui Province grant KJ2020ZD008. The work of Yanfang Ma was supported by the Natural Science Foundation of Anhui Province grant 2108085MF204 and the Natural Science Foundation of the Higher Education Institutions of Anhui Province grant KJ2019A0604. Part of this work was done while Liang Chen and Yanfang Ma were visiting scholars at Department of Mathematics, the University of Texas at Arlington from August 2019 to August 2020. They would like to thank Prof. Ren-Cang Li for his hospitality during the visit.

Conflict of interest

The authors declare no conflict of interest.