#### Abstract

Conjugate gradient methods are well-known methods which are widely applied in many practical fields. CD conjugate gradient method is one of the classical types. In this paper, a modified three-term type CD conjugate gradient algorithm is proposed. Some good features are presented as follows: (i) A modified three-term type CD conjugate gradient formula is presented. (ii) The given algorithm possesses sufficient descent property and trust region property. (iii) The algorithm has global convergence with the modified weak Wolfe–Powell (MWWP) line search technique and projection technique for general function. The new algorithm has made great progress in numerical experiments. It shows that the modified three-term type CD conjugate gradient method is more competitive than the classical CD conjugate gradient method.

#### 1. Introduction

Considering the problemwhere is a continuously differentiable function. This kind of model is often used to solve some problems in applied mathematics, economics, engineering, and so on. Generally, the following iteration formula is used to generate the next iteration point:where and denote the next iteration point and current iteration point, respectively. is a step-length and is the search direction. The search direction generated by Conjugated Gradient (CG) method is defined by the following formula:where is the gradient of at and is a parameter. Different will generate different CG methods [110]. There are six classical forms of :where , and is the Euclidean norm. Those formulas can be divided into two categories: One includes PRP method, HS method, and LS method, which have good numerical performance; the other includes FR method, CD method, and DY method, which have good theory convergence. About these methods, many scholars had applied them to solve nonlinear monotone equations and normal optimization problems, and some good results were achieved [1116].

Zhang et al. [17] presented a modified PRP CG formula as follows:

They proved that the modified PRP method is globally convergent with Armijo-type line search.

Yuan et al. [18] proposed another modified PRP CG formula withwhere and . Yuan et al. [18] obtained the global convergence of the modified PRP method with a modified weak Wolfe–Powell (MWWP) line search technique, which was proposed by Yuan et al. [19]:where , , and . Yuan et al. [16] obtained the global convergence of PRP method by using the above modified weak Wolfe–Powell line search technique and a projection technique:where is the next point and parameter . With this projection technique, any unsatisfactory iteration points generated by the normal PRP algorithm will be projected onto a surface to overcome the failure to converge.

Motivated by the above researches, a modified three-term CD conjugate gradient algorithm is presented with (7), (8), and (9) in this paper. Some good properties are obtained as follows:(i)A modified three-term type CD conjugate gradient formula is presented(ii)The given algorithm possesses sufficient descent property and trust region property(iii)The algorithm has global convergence with the MWWP line search technique and projection technique for general function

This paper is organized as follows: the next section will introduce the modified CD formula and relative algorithm; section 3 gives the proof of the global convergence of the new algorithm; numerical experiments are given in section 4; some conclusions are presented in section 5. Throughout this paper, we use to denote the Euclidean norm, and are replaced by and , respectively.

#### 2. Motivation and Algorithm

The convergence of CD conjugate gradient method has been proved [4]; however, the numerical results of this method are worse than the PRP method and others. Therefore, it is necessary to propose a new search direction to improve the numerical performance of the CD method. Meanwhile, the sufficient descent property is significant for obtaining the convergence of the conjugate gradient method:

Then, we also hope that we can propose a new method that possesses this property. Inspired by the above discussion, a modified three-term type CD conjugate gradient formula is designed by the following:where , and . The steps of the given algorithm are listed as follows.

#### 3. Convergence Analysis

In this section, we are going to analyse the convergence of the proposed algorithm. The following assumptions are needed.

Assumption 1. (i)The level set is bounded.(ii) is twice continuously differentiable and bounded below, and the gradient function is Lipschitz continuous, which means that there exists a constant such that

Lemma 1. Let the search direction be generated by formula (11), then, the following relations hold:where constants .

Proof. According to the definition of ,For the parameter , we have the following:then holds. Letting , the relation (13) is obtained.
Using the definition of the parameter , we analyse the value of in two cases:

Case 1. . Similar to Lemma 1 of [20], , where is a scalar; then

Case 2. . ThenLetting , we have . Thus,Let ; then (14) holds. The proof is complete.

Remark 1. The relation (14) shows that the optimization algorithm possesses the trust region feature.
The following theorem is obtained for the global convergence of Algorithm 1.

 Step 1: Given the initial point , constants , , , let , . Step 2: If , then, stop; otherwise, proceed to the next step. Step 3: Compute step size by the line search (7) and (8). Step 4: Let . Step 5: If holds, let , , and go to Step 7; otherwise, go to Step 6. Step 6: Let be defined by (9), , and . Step 7: If , stop; otherwise, proceed to next step. Step 8: Calculate the search direction by (11). Step 9: Let , and go to step 2.

Theorem 1. Assume that and are generated by Algorithm 1 and Lemma 1 holds. Then,

Proof. From the line search (8), we have the following:According to (ii) of Assumption 1,Then, we have the following:Using Lemma 1,From Assumption 1, line search (7) and sufficient descent property (13),Summing these inequalities from to ,From Assumption 1, it is easy to know that is bounded. Then, . The proof is complete.

#### 4. Numerical Results

This section will report the numerical experiments with some classical optimization problems, the nonlinear Muskingum model, and the application in image restoration problems. All the tests are coded in MATLAB R2014a, run on a PC with a 2.50 GHz CPU, and 4.00 GB of memory running the Windows 10 operating system.

##### 4.1. Normal Unconstrained Optimization Problems

In this subsection, the numerical experiments would be done with some test problems from [20], and all test problems are listed in Table 1. We compare Algorithm 1 with the classical CD conjugated method (called Algorithm 2) and the classical PRP conjugated method (called Algorithm 3). The detailed experimental data are list in Table 2. Figures 13 show the performance of these three algorithms related to CPU, NI, and NFG. Some columns of Tables 1 and 2 and Figures 13 have the following meanings:(i)No: the serial number of the problem.(ii)Dim: the dimension of the variable .(iii)NI: the iteration numbers.(iv)NFG: the sum of function value and gradient value.(v)CPU: the calculation time in seconds.(vi)Dimension: the dimensions are 3000, 9000, and 15000.(vii)Initialization: the parameters of the algorithms are chosen to be , , and , and the initial search direction .(viii)Stop rules: the following Himmeblau stop rule [21] is used: if , let ; otherwise, let . For every problem, if the conditions or are satisfied, then the program is stopped. This program is also stopped when the number of iterations is greater than one thousand.

From the detailed experimental data of Table 2, it is obvious to see that most of the problems can be solved quickly. For most of the problems, it takes less CPU time to solve those problems with the proposed algorithm. Meanwhile, progress has also been made in NI and NFG. Generally, the algorithm of the proposed method is promising versus the other algorithms. About the numerical results, the algorithm of Dolan and More [22] will be used to more directly show the performance profiles of these algorithms. In Figure 1, the curve of Algorithm 1 is always above the other algorithms. In Figure 2, the curves have the same trend. Algorithm 1 almost solves about of the test problems in , while Algorithm 2 just solves and Algorithm 3 just solves in . Figure 3 shows a similar trend as Figure 2. All the above pictures show that the modified CD conjugate gradient algorithm is more robust and effective compared with the normal CD method and PRP method. In summary, Algorithm 1 is more competitive versus others.

##### 4.2. The Muskingum Model

It is generally known that parameter estimation is a significant task in engineering applications. The nonlinear Muskingum model will be discussed as a common example of such an application in this subsection.

The Muskingum Model [23] is defined by the following:

Some of the variables have the following meanings:(i): the total time number.(ii): the storage time constant.(iii): the observed inflow discharge.(iv): the weighting factor.(v): the observed outflow discharges.(vi) : an additional parameter.(vii) : the time step at time .

In the experiment, the observed data of the flood run-off process from Chenggouwan and Linqing of Nanyunhe River in the Haihe Basin, Tianjin, China, are used. We choose the initial point ; detailed data about and for the years 1960, 1961, and 1964 were obtained (see [24] in detail). was selected. The results of these three algorithms are listed in Table 3. The performance of the presented algorithm is shown in Figures 46.

Some conclusions are obtained from this experiment: (1) from Figures 46, we conclude that we can calculate approximations of flood outflows by using Algorithm 1, and Algorithm 1 is effective for the nonlinear Muskingum model; (2) the final points (, and ) of these three algorithms are interesting, and they are competitive with the final points of similar algorithms; and (3) the final points of Algorithm 1 are different from those of the BFGS method and HIWO method, which implies that the nonlinear Muskingum model has different optimum approximation solutions.

##### 4.3. Image Restoration Problems

In this subsection, the above algorithms will be applied to image restoration problems. The original images corrupted by impulse noise are treated as objects here. These problems are regarded as one of the most difficult problems in optimization fields. Related parameter settings are similar to the above subsections, and the program will be stopped when the condition  or  holds. The following three images are selected as processing objects: Banoon (512512), Barbara (512512), and Lena (512512). The detail performances are shown in Figures 79. The CPU time taken to process images is listed in Table 4.

It is easy to see that all the algorithms are successful for image restoration problems. The results in Table 4 reveal that the CPU time of Algorithm 1 is less than that of other algorithms, whether for \$30\%\$ noise problems, noise problems, or for noise problems.

#### 5. Conclusion

In this paper, a modified three-term type CD conjugate gradient algorithm is presented. Some good features are also presented: (i) sufficient descent property holds, (ii) trust region feature also holds, (iii) the algorithm has global convergence with the MWWP line search technique and projection technique for general function, and (iv) numerical results reveal that the new algorithm is more competitive than the normal CD algorithm and PRP algorithm.

In recent years, there have been considerable researches about other types of CG method, while the study of CD method is not enough, and it should not be ignored. We have many works to do in the future: whether this method is suitable for other line search technique (such as Armijo line search, nonmonotone line search), or whether there exist other better modification methods to improve the numerical results of the CD method. All these are worth studying in the next work.

#### Data Availability

The data used to support the findings of this study are included within the article.

#### Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

#### Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant no. 11661009), the High Level Innovation Teams and Excellent Scholars Program in Guangxi Institutions of Higher Education (Grant No.[2019]52), and the Guangxi Natural Science Key Fund (No. 2017GXNSFDA198046). The authors would like to thank the editor and the referees for their valuable comments, which greatly improve this paper.