Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects

1.   Introduction

Let $M_1, M_2\ldots, M_ s\in\mathbb{R}^{n\times n}$, and let $u_1(z), u_2(z), \ldots, u_ s(z): \mathbb{R}^{n}\rightarrow \mathbb{R}^{n}$, be $s$ nonlinear mappings. The definition of the vertical nonlinear complementarity problem (abbreviated as VNCP) is: Find $z\in\mathbb{R}^{n}$ such that

Here, the minimum operation is taken component-wise.

The VNCP has many applications in generalized Leontief input-output models, control theory, nonlinear networks, contact problems in lubrication, and so on; e.g., see ^{[11,18,34,35,39]}. Taking $u_i(z) = u_i\in\mathbb{R}^{n}, 1\leq i\leq s$, the VNCP reduces to the vertical linear complementarity problem (abbreviated as VLCP) ^[9,20,40]. Further, taking $s = 1$, the VLCP reduces to the linear complementarity problem (abbreviated as LCP) ^[10].

In earlier literatures, there were some iterative methods for such problems. In ^[39], the Mangasarian's general iterative algorithm was constructed to solve the VLCP. Solving an approximation equation of the nonlinear complementarity problem by a continuation method can be extended to solve the VNCP; see ^[8]. Furthermore, in ^[37] the authors approximated the equivalent minimum equation of the VNCP by a sequence of aggregation functions, and found the zero solution of the approximated problems. Moreover, some interior-point methods were applied to solve complementarity problems, such as LCP ^[24,29], nonlinear complementarity problems (abbreviated as NCP) ^[36] and horizontal LCP ^[46]. In recent years, modulus-based matrix splitting (abbreviated as MMS) iteration methods have gained popularity for numerical solutions to various complementarity problems. References ^[2,12,15,16] detail their application to the LCP, while ^[13,32,50] focus on the horizontal LCP. The second-order cone LCP is discussed in ^[27], implicit complementarity problems in ^[7,14,25], quasi-complementarity problem in ^[38], NCP in ^[41], and the circular cone NCP is addressed in ^[28]. Numerical examples have demonstrated that MMS iteration methods often outperform state-of-the-art smooth Newton methods in practical applications. Specifically, for the VLCP, a special case of the VNCP, the MMS iteration method was introduced in ^[31]. Alternatively, an MMS iteration method without auxiliary variables based on a non-auxiliary-variable equivalent modulus equation was presented in ^[23] and shown to be more efficient than the method in ^[31]. Accelerated techniques and improved results for MMS iteration methods in the VLCP are further detailed in ^{[21,48,49,51]}. On the other hand, Projected type methods were also used to solve the VLCP; see ^[6,33]. For the VNCP, the only literature on the MMS iteration method currently is ^[42].

To improve the convergence rate of the MMS iteration method for solving the VNCP, in this work, we aim to construct a two-step MMS iteration method. The two-step splitting technique had been successfully used in other complementarity problems, e.g., see ^{[43,44,45,52]}, where the main idea is to change the iteration in the MMS to two iterations based on two matrix splittings of the system matrices, which can make full use of the information of the matrices for acceleration.

In the following, after presenting some required preliminaries, the new two-step MMS iteration method is established in Section 2. The convergence analysis of the proposed method is given in Section 3, which can generalize and improve the results in ^[42]. Some numerical examples are presented to illustrate the efficiency of the proposed method in Section 4, and concluding remarks of the whole work are presented in Section 5.

2.   New method

First, some notations, definitions, and existing results needed in the following discussion are introduced.

Let $M = (m_{ij})\in\mathbb{R}^{n\times{n}}$ and $M = D_M-B_M$, where $D_M$ and $-B_M$ are the diagonal and the nondiagonal matrices of $M$, respectively. For two matrices $M$ and $N$, the order in inequality $M\geq(>) N$ means $m_{ij}\geq(>)n_{ij}$ for every $i, j$. Let $\lvert M\rvert = (\lvert m_{ij}\rvert)$ and the comparison matrix of $M$ be denoted by $\langle{M}\rangle = (\langle{m_{ij}}\rangle)$, where $\langle{m_{ij}}\rangle = \lvert m_{ij}\rvert$ if $i = j$ and $\langle{m_{ij}}\rangle = -\lvert m_{ij}\rvert$ if $i\neq{j}$. If $m_{ij}\leq0$ for any $i\neq{j}$, $M$ is called a $Z$-matrix. If $M$ is a nonsingular $Z$-matrix and $M^{-1}\geq0$, it is called a nonsingular $M$-matrix. $M$ is called an $H$-matrix if $\langle{M}\rangle$ is a nonsingular $M$-matrix. If $\lvert m_{ii}\rvert > \sum\limits_{j\neq i}\lvert m_{ij}\rvert$ for all $1\leq i\leq n$, $M$ is called a strictly diagonal dominant (abbreviated as s.d.d.) matrix (see ^[5]). If $M$ is an $H$-matrix with all its diagonal entries positive (e.g., see ^[1]), it is called an $H_+$-matrix. If $M$ is a nonsingular $M$-matrix, it is well known that there exists a positive diagonal matrix $\Lambda$, which can make $M\Lambda$ be an s.d.d. matrix with all the diagonal entries of $A\Lambda$ positive ^[5]. $M = F-G$ is called an $H$-splitting if $\langle F\rangle-\lvert G\rvert$ is a nonsingular $M$-matrix ^[19].

Let $\mathcal{Y} = \{Y_1, Y_2, \ldots, Y_s\}$ be a set of $n\times n$ real matrices (or $n\times 1$ real vectors). Denote a mapping $\varphi$ as follows:

Let $\Omega\in\mathbb{R}^{n\times n}$ be a positive diagonal matrix, $\sigma > 0\in\mathbb{R}$ and $M_i = F_i-G_i$ $(1\leq i\leq s)$ be $s$ splittings of $M_i$. Denote

and

By equivalently transforming the VNCP to a fixed-point equation, the MMS iteration method for the VNCP was given in ^[42].

Method 2.1. ^[42] (MMS) Given $x_1^{(0)}\in \mathbb{R}^{n}$, for $k = 0, 1, 2, \ldots$, compute $x_1^{(k+1)}\in \mathbb{R}^{n}$ by

where

and $x_2^{(k)}, \ldots, x_ s^{(k)}$ are computed by

until the iteration is convergent.

Based on Method 2.1, to fully utilize the information of the entries of the matrix set $\mathcal{M}$, for $1\leq i\leq s$, consider two matrix splittings of $M_i$, e.g., $M_i = F_i^{(1)}-G_i^{(1)} = F_i^{(2)}-G_i^{(2)}$. Then, the two-step MMS (abbreviated as TMMS) iteration method can be established as follows:

Method 2.2. (TMMS) Given $x_1^{(0)}\in \mathbb{R}^{n}$, for $k = 0, 1, 2, \ldots$, compute $x_1^{(k+1)}\in \mathbb{R}^{n}$ by

where

where $x_2^{(k)}, \ldots, x_ s^{(k)}$ are computed by (2.1) and

until the iteration is convergent.

Clearly, if we take $F_i^{(1)} = F_i^{(2)}$, Method 2.2 reduces to Method 2.1 immediately. Furthermore, we can obtain a class of relaxation methods from Method 2.2 by specially choosing the two matrix splittings, similar to those in ^{[43,44,45,49,50,52]}. For example, for $i = 1, 2, \ldots, s$, taking

one can get the two-step modulus-based accelerated overrelaxation (abbreviated as TMAOR) iteration method, which can reduce to the two-step modulus-based successive overrelaxation (abbreviated as TMSOR) and Gauss-Seidel (abbreviated as TMGS) methods, when $(\alpha, \beta) = (\alpha, \alpha)$ and $(\alpha, \beta) = (1, 1)$, respectively.

3.   Convergence analysis

In this section, the convergence conditions of Method 2.2 are given under the assumption that the VNCP has a unique solution $z^*$, the same as that in ^[42]. Furthermore, for $1\leq i\leq s$, $u_i(z)$ is assumed to satisfy the locally Lipschitz smoothness conditions: Let

be differentiable with

Then, by Lagrange mean value theorem, there exists $\xi_j$ between $z_j^{(k)}$ and $z_j^*$ such that

where ${U}_i^{(k)}$ is a nonnegative diagonal matrix with diagonal entries $\frac{\partial u_i(z_j)}{\partial z_j}\bigg{|}_{z_j = \xi_j}, 1\leq j\leq n$. Furthermore, by (3.1), we have

Denote

Lemma 3.1. Let $M_i, 1\leq i\leq s$, be $H_+$-matrices, $\Omega\in\mathbb{R}^{n\times n}$ be a positive diagonal matrix, and $\sigma > 0\in\mathbb{R}$. For $t = 1, 2$, assume that:

(I) $D_{F_i^{(t)} } > 0, i = 1, 2, \ldots, s-1$, and $M_ s = F_ s^{(t)} -G_ s^{(t)}$ be an $H$-splitting of $M_ s$;

(II)

(III) There exists a positive diagonal matrix $\Lambda$ such that $(\langle F_ s^{(t)} \rangle-\lvert G_ s^{(t)} \rvert)\Lambda$ is an s.d.d. matrix.

Then, $2^{ s-1}\Omega+\varphi(\mathcal{F}^{(t)})$ is an $H$-matrix.

Proof. Let $e = \in\mathbb{R}^{n}$ be a vector with all entries being 1. By reusing (3.4) $s-2$ times, we have

Hence, $\langle 2^{ s-1}\Omega+\varphi(\mathcal{F}^{(t)})\rangle \Lambda$ is an s.d.d matrix, which implies that $2^{ s-1}\Omega+\varphi(\mathcal{F}^{(t)})$ is an $H$-matrix. □

Lemma 3.2. With the same notations as those in Lemma 3.1, denote $x_i^*, i = 1, 2, \ldots, s$, as the solution of the VNCP, and let

Then, we have

where

and

Proof. By (2.1), we get

By the first equation of (3.6), we have

Then, when the subscript is $s-1$, with the second equation of (3.6), we can get

By induction, for $2\leq i\leq s$, we have

Then, by (3.7), we get

□

Theorem 3.1. With the same notations and assumptions as those in Lemmas 3.1 and 3.2, for $t = 1, 2$, assume that

Then, Method 2.1 converges for any initial vector $x_1^{(0)}$ provided that

or

Proof.

By (3.2), we get

By the definition of $\varphi$, we have

Combining the first equation of (2.2) and (3.11), we can get

Similarly, by the second equation of (2.2), we have

Then we have

By Lemma 3.1, we have that $2^{ s-1}\Omega+\varphi(\mathcal{F})$ is an $H$-matrix. By ^[17], with (3.5) and (3.12), we get

where

First, we estimate ${\mathcal{F}^{(1)}}^{-1}\mathcal{G}^{(1)}$. By Lemma 3.1 and ^[26], we have

Let

and

Clearly, we have $\Gamma_j\leq \Gamma_j^{(1)}+\Gamma_j^{(2)}+\Gamma_j^{(3)}$. By (3.4), we can get

Then, by direct computation, we can obtain

Moreover, by reusing (3.4) $s-2$ times, we get

Analogously, by reusing (3.8) $s-2$ times, we get

By (3.15)–(3.17), we get

Next, consider the following two cases.

Case 1. When

by (3.18), we have

Case 2. When

by (3.18), we have

Summarizing Cases 1 and 2, we immediately get $\mathcal{P}^{(1)}\Lambda e-\mathcal{Q}^{(1)}\Lambda e > 0$, provided that (3.9) or (3.10) holds, which implies that

by (3.14).

By similar deductions, we can also get

when (3.9) or (3.10) is satisfied.

In summary, the spectral radius of the iteration matrix given by (3.13) can be estimated as follows:

Hence, we can see that $\{x_1^{(k)}\}_{k = 0}^{\infty}$ converges by (3.13). □

Based on Theorem 3.1, we have the next theorem for the TMAOR method.

Theorem 3.2. With the same notations and assumptions as those in Theorem 3.1, for $t = 1, 2$, let $F_i^{(t)}$ and $G_i^{(t)}$ be given by (2.4), $1\leq i\leq {s}$. Assume that

Then, the TMAOR converges to the solution of the VNCP.

Proof. By (2.4), with direct computation, we can get

if $0 < \beta\leq\alpha$. Since $M_s$ is an $H_+$-matrix, by ^[5] we have $\rho(D_{M_s}^{-1}{\mid}B_{M_s}{\mid}) < 1$. Since (3.19) holds, we can easily have that $\frac{1-{\mid}1-\alpha{\mid}}{\alpha}D_{M_s}-{\mid}B_{M_s}{\mid}$ is a nonsingular $M$-matrix. Note that all the assumptions of Theorem 3.1 hold. Hence, the TMAOR is convergent by Theorem 3.1. □

4.   Numerical examples

Next, some numerical examples are given to shown the efficiency of Method 2.2 compared to Method 2.1.

Example 4.1. ^[42] Consider a VNCP ($s = 2$), where the two system matrices are given by

where

Example 4.2. ^[42] Consider a VNCP ($s = 2$), where the two system matrices are given by

where

Example 4.3. Consider the VNCP ($s = 2$) whose system matrices $M_1, M_2\in\mathbb{R}^{m^2\times m^2}$ come from the discretization of Hamilton-Jacobi-Bellman (HJB) equation ^[4]

with $\Gamma = \{(x, y)\mid0 < x < 2, 0 < y < 1\}$,

Same as those in ^[42], all the nonlinear functions in Examples 4.1–4.3 are set to $u_1(z) = \frac{z}{1+z}$ and $u_2(z) = arctan(z)$.

The programming language is MATLAB, whose codes are run on a PC with a 12th Gen Intel(R) Core(TM) i7-12700 2.10 GHz and 32G memory. Consider the Gauss-Seidel (abbreviated as GS), SOR and AOR splittings, where the SOR splitting is

with $\alpha$ being the relaxation parameter and starting from 0.8 to 1.2 with a step size of 0.1, while the parameters of AOR splitting given by (2.4) satisfy $\alpha = \beta+0.1$, and $\beta$ starts from 0.8 to 1.2 with a step size of 0.1.

All initial vectors are set to $x_1^{(0)} = e$. The stopping criterion in the iteration of both Methods 2.1 and 2.2 is taken as

and $\Omega$ is chosen as

Let $m = 256,512, 1024$ for the three examples. For fair comparison, when the relaxation parameters are chosen as the experimentally optimal ones for each size of the three examples, the results of the MMS and TMMS methods are shown in Tables 1–3, where "IT" is the iteration steps, "CPU" is the elapsed CPU time, and

It is found from the numerical results that Method 2.2 always converges faster than Method 2.1 for all cases. Specifically, the iteration steps of Method 2.1 take almost twice as much time as those of Method 2.2 due to the fact that there are two equations needed to solve in each iteration in Method 2.2 versus only one in Method 2.1. Focusing on the CPU time, the cost of Method 2.2 is 14%–23% less than that of Method 2.1 for Example 4.1, while the percentages of Examples 4.2 and 4.3 are 21%–38% and 7%–18%, respectively. It is clear that the two-step splitting technique works for accelerating the convergence of the MMS iteration method.

5.   Conclusions

By the two-step matrix splitting technique, we have constructed the TMMS iteration method for solving the VNCP. We also present the convergence theorems of the TMMS iteration method for an arbitrary $s$, which can extend the research scope of the modulus-based methods for the VNCP.

Note that to show the effectiveness of the proposed two-step method, the choice of the two-step splittings in Section 4 is common in existing literatures. However, since the iteration matrix in (3.13) is essentially an extended bound containing absolute operation, it is very hard to minimize its spectral radius. How to choose an optimal two-step splitting in general or based some special structure is still an open question. On the other hand, it is noted that there were some other accelerated techniques for the MMS iteration method, such as relaxation, precondition, two-sweep and so on. One can also mix a certain technique with the two-step splittings for acceleration, e.g., ^[3,22,30,47]. More techniques to improve the convergence of the MMS iteration method are worth studying in the future.

Use of AI tools declaration

The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

Acknowledgement

This work was supported by Basic and Applied Basic Research Foundation of Guangdong (No. 2024A1515011822), Scientific Computing Research Innovation Team of Guangdong Province (No. 2021KCXTD052), Science and Technology Development Fund, Macau SAR (No. 0096/2022/A), Guangdong Key Construction Discipline Research Capacity Enhancement Project (No. 2022ZDJS049) and Technology Planning Project of Shaoguan (No. 230330108034184).

Conflict of interest

The authors declare that they have no conflict of interest.