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A new accelerated conjugate gradient method for largescale unconstrained optimization
Journal of Inequalities and Applications volume 2019, Article number: 300 (2019)
Abstract
In this paper, we present a new conjugate gradient method using an acceleration scheme for solving largescale unconstrained optimization. The generated search direction satisfies both the sufficient descent condition and the Dai–Liao conjugacy condition independent of line search. Moreover, the value of the parameter contains more useful information without adding more computational cost and storage requirements, which can improve the numerical performance. Under proper assumptions, the global convergence result of the proposed method with a Wolfe line search is established. Numerical experiments show that the given method is competitive for unconstrained optimization problems, with a maximum dimension of 100,000.
1 Introduction
Consider the following unconstrained optimization problem:
where \(f:\mathbb{R}^{n} \rightarrow \mathbb{R}\) is a continuously differentiable function, bounded below and its gradient is denoted by \(g(x)=\nabla f(x)\). Conjugate gradient methods characterized by simplicity and low storage are efficient for solving (1), especially when the dimension n is large.
Starting from an initial guess \(x_{0}\in \mathbb{R}^{n}\), the conjugate gradient methods use the recurrence
where \(x_{k+1}\) is the current iterate, \(\alpha _{k}>0\) is the steplength, which is obtained by some line search, and \(d_{k}\) is the search direction determined by
where \(g_{k}=g(x_{k})\). The scalar \(\beta _{k}\) is called the conjugate gradient parameter. There are many formulas to construct the scalar \(\beta _{k}\), such as \(\beta _{k}^{\mathrm{{PRP}}}\) [28, 29], \(\beta _{k}^{\mathrm{{HS}}}\) [19] and \(\beta _{k}^{\mathrm{{FR}}}\) [16].
The line search in conjugate gradient methods is usually based on the general Wolfe conditions [33, 34],
where \(d_{k}\) is a descent direction and the constants ρ, σ satisfy \(0< \rho \leq \sigma \leq 1\). However, in order to establish the convergence and enhance the stability, the strong Wolfe conditions given by (4) and
are needed.
Recently efforts have been made to modify conjugate gradient methods for minimizing unconstrained optimization. In a natural way, Dai and Liao [8] extended the classical conjugate condition \(y_{k}^{\mathrm{T}}d_{k+1}=0\) to
where \(s_{k}=x_{k+1}x_{k}\), \(y_{k}=g_{k+1}g_{k}\) and t is a positive parameter. Based on the Dai–Liao conjugacy condition (7), [8] introduced the conjugate gradient parameter \(\beta _{k}^{\mathrm{{DL}}}\) as follows:
Having applied modified secant equations, many researchers have derived various conjugate gradient methods [7, 17, 21, 22, 25, 26, 30, 35, 44]. Moreover, combining with a quasiNewton updating technique, conjugate gradient methods can be considered as a special type of quasiNewton methods. From (3), (7) and Perry’s point of view in [27], we can rewrite the search direction as follows:
where
Obviously, the Dai–Liao method can be considered as a special type of quasiNewton method in which the matrix \(Q_{k+1}\) is used to approximate the inverse Hessian of the objective function. Since the matrix \(Q_{k+1}\) is nonsymmetric and does not satisfy the secant condition, (9) cannot be regarded as a quasiNewton direction from a strict point of view.
To overcome the above shortcomings and improve the numerical performance of conjugate gradient methods, Andrei [1, 2] proposed the following matrix \(Q_{k+1}^{\mathrm{A}}\):
to replace the matrix \(Q_{k+1}\) in (10). The parameter t in the last term on the righthand side is calculated with \(t=1+\frac{\y_{k}\^{2}}{y_{k}^{\mathrm{T}}s_{k}}\) and \(t=1+2\frac{ \y_{k}\^{2}}{y_{k}^{\mathrm{T}}s_{k}}\), corresponding to the THREECG method [1] and the TTCG method [2], respectively. The search directions satisfy not only the descent condition but also the conjugacy condition, independent of the line search. Both of the methods can be regarded as modifications of the classical HS or of the CG_DESCENT conjugate gradient methods. Numerical results support this claim.
Motivated by [1] and [2], Deng and Wan [14] presented a symmetric matrix to estimate the inverse Hessian approximation as follows:
where \(t=1\frac{\y_{k}\^{2}}{y_{k}^{\mathrm{T}}s_{k}}\). The search direction in this method (MTHREECG) is close to the Newton direction and satisfies the Dai–Liao conjugacy condition. Then they restricted \(t=1\min \{1, \frac{\y_{k}\^{2}}{y_{k}^{\mathrm{T}}s_{k}}\}\) and obtained the descent property. Numerical results show that the MTHREECG method outperforms the THREECG method and the CG_DESCENT method.
More recently, Yao and Ning [37] suggested the following symmetric matrix:
where the positive parameter \(t_{k}\) is determined by minimizing the distance of \(Q_{k+1}^{\mathrm{C}}\) and the selfscaling memoryless BFGS matrix in the Frobenius norm. In this method (NTAP), they let \(a_{k}= \frac{\s_{k}\^{2}\y_{k}\^{2}}{(s_{k}^{\mathrm{T}}y_{k})^{2}}\), then \(t_{k}\) can be expressed as \(t_{k}=\min \{\frac{1}{1+a_{k}},\frac{s _{k}^{\mathrm{T}}y_{k}}{\y_{k}\^{2}}\}\). The sufficient descent property of the search direction depends neither on the line search, nor on the convexity of objective function. For relevant research see [3,4,5,6, 10,11,12,13, 20, 23, 24, 31, 32, 36, 38,39,40,41,42,43].
By focusing on the above research, we are interested in developing a new accelerated conjugate gradient method (NACG) for largescale unconstrained optimization. The generated search direction satisfies both sufficient descent condition and Dai–Liao conjugacy condition. The parameter in the given method provides more useful information and adds no extra computational and storage burden. In addition, the proposed method has an obvious improvement in computational performance, especially in dealing with largescale unconstrained optimization problems.
The rest of this paper is organized as follows. In the next section, we will describe the framework of the new method and the choice of parameter in generated search direction. Global convergence results of the obtained method will be established under appropriate conditions in Sect. 3. Section 4 is devoted to numerical experiments and comparisons with some other efficient conjugate gradient algorithms for solving unconstrained optimization problems with different dimensions. Conclusions are drawn in Sect. 5.
2 The NACG method
In this section, we state our new accelerated conjugate gradient method exploiting BFGS updating technology, for which at each step both the sufficient descent condition and the Dai–Liao conjugacy condition are satisfied, independent of the line search.
It is well known that the BFGS method is one of the most efficient quasiNewton methods. By introducing two adaptive parameters for adjusting, we give the following matrix:
where the parameters \(t_{k_{1}}\) and \(t_{k_{2}}\) are determined in the following.
In a sense, the method of form (9) and (14) could be considered as a selfadaptive memoryless BFGS method. Substituting (14) into (9), we get
Let
then (15) takes the form of threeterm conjugate gradient,
In what follows, we discuss the choices for the two parameters \(t_{k_{1}}\) and \(t_{k_{2}}\). The parameters are selected in such a manner that the Dai–Liao conjugacy condition and the sufficient descent condition are satisfied from iteration to iteration.
Consider the Dai–Liao conjugacy condition (7) with \(t=1\), i.e.,
Substituting (15) into (19), by simple calculation, we obtain
which yields
Since the descent property of the search direction, \(g_{k+1}^{ \mathrm{T}}d_{k+1}<0\), is crucial for the convergence analysis. It is easy to see
If we restrict \(t_{k_{1}}< 1\) and take
then \(g_{k+1}^{\mathrm{T}}d_{k+1}<0\) holds. Substituting (21) into (20), we have
If \(1\frac{s_{k}^{\mathrm{T}}g_{k+1}}{y_{k}^{\mathrm{T}}g_{k+1}} \geq 1\), we set \(t_{k_{1}}=0\). This implies a restarted scheme. Taking into consideration the acceleration technique, the new accelerated conjugate gradient method (NACG) can be suggested.
Algorithm 1
(NACG)

Step 0.
Choose an initial point \(x_{0} \in \mathbb{R}^{n}\), \(\varepsilon >0\), and compute \(f_{0}=f(x_{0})\), \(g_{0}=\nabla f(x_{0})\). Set \(d_{0}:=g_{0}\) and \(k:=0\).

Step 1.
If \(\g_{k}\<\varepsilon \), stop, else go to Step 2.

Step 2.
Compute a steplength \(\alpha _{k}\) by Wolfe line search (4) and (5).

Step 3.
Compute \(x_{k+1}\) by the acceleration scheme,
 3.1.
Compute \(z=x_{k}+\alpha _{k}d_{k}\), \(g_{z}=\nabla f(z)\) and \(y_{z}=g_{k}g_{z}\);
 3.2.
Compute \(\bar{a}_{k}=\alpha _{k}g_{k}^{\mathrm{T}}d_{k}\) and \(\bar{b}_{k}=\alpha _{k}y_{k}^{\mathrm{T}}d_{k}\);
 3.3.
Acceleration scheme. If \(\bar{b}_{k}>0\), then compute \(\xi _{k}= \bar{a}_{k}/\bar{b}_{k}\) and update the variables as \(x_{k+1}=x_{k}+ \xi _{k}\alpha _{k}d_{k}\), otherwise update the variables as \(x_{k+1}=x _{k}+\alpha _{k}d_{k}\).
 3.1.

Step 4.
Compute \(f_{k+1}=f(x_{k+1})\), \(g_{k+1}=g(x_{k+1})\), \(s_{k}=x_{k+1}x_{k}\) and \(y_{k}=g_{k+1}g_{k}\).

Step 5.
Compute \(s_{k}^{\mathrm{T}}g_{k+1}\), \(y_{k}^{\mathrm{T}}g_{k+1}\), \(y_{k}^{\mathrm{T}}s_{k}\) and \(y_{k}^{ \mathrm{T}}y_{k}\), respectively.

Step 6.
Compute \(t_{k_{1}}\) and \(t_{k_{2}}\) by
$$ t_{k_{1}}= \textstyle\begin{cases} 1\frac{s_{k}^{\mathrm{T}}g_{k+1}}{y_{k}^{\mathrm{T}}g_{k+1}},& \mbox{if } 0< \frac{s_{k}^{\mathrm{T}}g_{k+1}}{y_{k}^{\mathrm{T}}g_{k+1}}< 2, \\ 0, & \mbox{else} , \end{cases} $$(23)and
$$ t_{k_{2}}=t_{k_{1}}\frac{y_{k}^{\mathrm{T}}y_{k}}{y_{k}^{\mathrm{T}}s _{k}}, $$(24)respectively.

Step 7.
Compute \(a_{k}\) and \(b_{k}\) by (16) and (17), respectively.

Step 8.
Set \(d_{k+1}=g_{k+1}+a_{k}s_{k}+b_{k}y_{k}\). Set \(k:=k+1\) and go to Step 1.
In Algorithm 1, Step 3 corresponds to the acceleration scheme. In Step 6, the parameter \(t_{k_{1}}\) defined by (23) satisfies \(t_{k_{1}}<1\), and the parameter \(t_{k_{2}}\) could be determined by the equality with \(t_{k_{1}}\). Furthermore, the main computational cost lies in \(s_{k}^{\mathrm{T}}g_{k+1}\), \(y_{k}^{\mathrm{T}}g_{k+1}\), \(y_{k}^{\mathrm{T}}s_{k}\) and \(y_{k}^{\mathrm{T}}y_{k}\) in Step 5. It costs \(O(4n)\) operations to compute the values of \(t_{k_{1}}\) and \(t_{k_{2}}\), and further get the values of \(a_{k}\) and \(b_{k}\). No additional storage cost is required during the calculation. Compared with the existing effective algorithms TTCG [2], MTHREECG [14] and NTAP [37], the TTCG and the MTHREECG require \(O(4n)\) operations, while the NTAP requires \(O(5n)\) operations. In one word, our algorithm NACG is competitive in computational cost.
The sufficient descent condition and Dai–Liao conjugacy condition of the generated search direction holds independent of line search, a concept we discuss next.
Lemma 2.1
Suppose that the search direction \(d_{k+1}\)is generated by Algorithm 1. Then \(d_{k+1}\)shows sufficient descent, i.e., \(g_{k+1}^{\mathrm{T}}d_{k+1}\leq c\g_{k+1}\^{2}\), where the constant \(c>0\).
Proof
Combining with (23) and (24), it follows that \(g_{k+1}^{\mathrm{T}}d_{k+1}\leq c\g_{k+1}\^{2}\) with \(c:=1t_{k _{1}}>0\). The proof is completed. □
Lemma 2.2
Suppose that the search direction \(d_{k+1}\)is generated by Algorithm 1. Then \(d_{k+1}\)satisfies the Dai–Liao conjugacy condition (19).
Proof
Substituting (23) and (24) into the above equality yields
which completes the proof. □
3 Convergence analysis
In this section, under appropriate assumptions, the global convergence of Algorithm 1 is established. Without loss of generality, we make the following basic assumptions.
Assumption (i)
The level set
is bounded, i.e., there exists a constant \(B>0\) such that
Assumption (ii)
The function \(f:\mathbb{R}^{n}\rightarrow \mathbb{R}\) is continuously differentiable and its gradient is Lipschitz continuous in a neighborhood \(\mathbb{N}\) of Ω, i.e., there exists a constant \(L>0\) such that
Under the above assumptions, we can easily see that there exists a constant \(\varGamma >0\) such that
Although the search direction \(d_{k+1}\) generated by Algorithm 1 is always a descent direction, in order to obtain the convergence of Algorithm 1, we need to derive a lower bound for the steplength \(\alpha _{k}\).
Lemma 3.1
Suppose that Assumption (ii)holds and \(\{d_{k}\}\)is generated by Algorithm 1. Then the steplength \(\alpha _{k}\)satisfies
The following lemma is called the Zoutendijk condition [45], which is often used to prove global convergence of conjugate gradient methods.
Lemma 3.2
Suppose that the assumptions hold. Consider the algorithm (2) and (18), where \(d_{k}\)is a descent direction and \(\alpha _{k}\)is obtained by a Wolfe line search (4) and (5). Then
The next lemma shows the sequence of gradient norms \(\g_{k}\\) is bounded away from zero only if \(\sum_{k\geq 0}1/\d_{k}\<+\infty \) for any conjugate gradient methods with strong Wolfe line search (4) and (6).
Lemma 3.3
Suppose that the assumptions hold. Consider the algorithm (2) and (18), where \(d_{k}\)is a descent direction and \(\alpha _{k}\)is obtained by a strong Wolfe line search (4) and (6). If
then
The proofs of Lemmas 3.1–3.3 refer to [1, 2], which are omitted here.
For uniformly convex functions, we establish the following global convergence result of Algorithm 1.
Theorem 3.1
Suppose that the assumptions hold. Let \(\{x_{k}\}\)and \(\{d_{k}\}\)be generated by Algorithm 1. Iffis a uniformly convex function onΩ, i.e., there exists a constant \(\mu >0\)such that
then
Proof
From (27), it follows that
From (33), we have
By the use of the Cauchy inequality and (36), it is obvious that \(\mu \s_{k}\^{2}\leq y_{k}^{\mathrm{T}}s_{k}\leq \y_{k}\\s _{k}\\), i.e.,
On the other hand, from the definition of \(a_{k}\) and \(b_{k}\) in (16) and (17), we obtain
and
Therefore, using (39) and (40) in (18), we get
showing that (31) holds. From Lemma 3.3, it follows that (32) is true, which for uniformly convex functions is equivalent to (34). The proof is completed. □
4 Numerical results
In this section, we report the numerical results for some unconstrained problems from [9] to show the efficiency of Algorithm 1 (NACG). All codes are written in Matlab R2013a and ran on PC with 1.80 GHz CPU processor and 8.00 GB RAM memory.
We compare NACG against TTCG [2], MTHREECG [14] and NTAP [37], which have a similar structure in search direction and have been reported to be superior to the classical PRP method, HS method and CGDESCENT [18] method, etc.
The iteration is terminated by the following condition:
All algorithms have the same stopping criteria. We set the parameters as \(\varepsilon =10^{6}\) in (42), and \(\rho =0.0001\), \(\sigma =0.8\) in a Wolfe line search (4) and (5). The other parameters are set as default. Table 1 lists the test problems and their dimensions.
According to a comparison of four algorithms for the 300 test problems with different dimensions, we can see that there is only one problem that the NACG and the MTHREECG cannot solve, while the TTCG does 98 percent of problems and the NTAP does 84.2 percent of problems, respectively.
We employ the profiles by Dolan and Moré [15] to analyze the efficiency of the NACG. In a performance profile plot, the horizontal axis gives the percentage (τ) of the test problems for which a method is the fastest (efficiency), while the vertical side gives the percentage (ψ) of the test problems that are successfully solved by each of the methods. Consequently, the top curve is the method that solved the most problems in a time that is within a factor of the best time.
Figures 1–4 plot the performance profiles for the number of iterations \((k)\), the CPU time \((t)\), the number of function evaluations \((nf)\) and the number of gradient evaluations \((ng)\), respectively.
From Fig. 1, it is obvious that the NACG exhibits the best performance subject to the number of iterations. For example, the NACG outperforms in 129 problems, the MTHREECG outperforms in 66 problems, while the other two methods outperform in 48 problems and 57 problems, respectively.
We see from Fig. 2 that the curve “MTHREECG” and “TTC” are very close, which are worse than the “NACG”. The NACG occupies the first place, which solves about 57% of the 300 test problems with the least CPU time.
Figures 3 and 4 show that if the values of τ are controlled in the range of 1 to 4, the curve “NACG” is always on the top, which means that our new algorithm is competitive relative to function evaluations and gradient evaluations, respectively. Until we expand the tolerance, the performance of the MTHREECG and TTCG are almost as same as that of the NACG, while the curve “NTAP” is at the bottom all the time.
In one word, all numerical performances indicate that the efficiency and stability of the NACG is promising, even if the dimensions of the test problems exceed 5000. Moreover, we conclude that the restarted scheme is called rarely from the numerical results.
If program runs failure, or the number of iterations reaches more than 500, or precision exceeds the optimal precision in the same test problem 10^{3} times or more, regarded as failed. Then we denote the number of iterations, function evaluations, gradient evaluations by 500 and CPU time by 10 seconds, respectively. In this way, the numerical results indicate that the algorithm NACG is encouraging.
5 Conclusions
Conjugate gradient methods are widely used for solving largescale unconstrained optimization problems, due to their simplicity and low storage. We employed the idea of BFGS quasiNewton method to improve the performance of conjugate gradient methods. Without affecting the amount of calculation and storage, the choice of the parameter in the proposed method provides more useful information. The generated search direction is close to a quasiNewton direction and fulfills not only the sufficient descent condition, but also the Dai–Liao conjugacy condition. Furthermore, under proper conditions, we prove the global convergence of the proposed method with Wolfe line search. For a set of 300 test problems, compared with the existing effective methods, the performance profiles show that the proposed method is promising for largescale unconstrained optimization.
It is worth emphasizing that conjugate gradient methods combining with BFGS updating technique represent an interesting computational innovation which produce efficient conjugate gradient algorithms. Our future work will be concentrated on developing some new methods to obtain superlinear convergence and extending the convergence results to general functions.
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This work is supported by the Innovation Talent Training Program of Science and Technology of Jilin Province of China (20180519011JH), and the Science and Technology Development Project Program of Jilin Province (20190303132SF).
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Chen, Y., Cao, M. & Yang, Y. A new accelerated conjugate gradient method for largescale unconstrained optimization. J Inequal Appl 2019, 300 (2019). https://doi.org/10.1186/s1366001922389
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DOI: https://doi.org/10.1186/s1366001922389