Research Article  Open Access
On the Strong Convergence of a Sufficient Descent PolakRibièrePolyak Conjugate Gradient Method
Abstract
Recently, Zhang et al. proposed a sufficient descent PolakRibièrePolyak (SDPRP) conjugate gradient method for largescale unconstrained optimization problems and proved its global convergence in the sense that when an Armijotype line search is used. In this paper, motivated by the line searches proposed by Shi et al. and Zhang et al., we propose two new Armijotype line searches and show that the SDPRP method has strong convergence in the sense that under the two new line searches. Numerical results are reported to show the efficiency of the SDPRP with the new Armijotype line searches in practical computation.
1. Introduction
In this paper, we are concerned with the following unconstrained minimization problem: where is a smooth function whose gradient is often denoted by . The related problem is called largescale minimization problem when its dimension is very large (e.g., ). For solving largescale minimization problems, the matricesfree methods are quite efficient. Among such methods, the conjugate gradient method is very famous for its excellent numerical performance in the practical computation. Much progress has been achieved in the study of global convergence of the various conjugate gradient methods, such as the PolakRibièrePolyak (PRP) [1, 2], the FletcherReeves (FR) [3], the HestenesStiefel (HS) [4, 5], and the DaiYuan (DY) [6] conjugate gradient methods, et al.
Recently, Zhang et al. [7] presented a sufficient descent PolakRibièrePolyak (SDPRP) conjugate gradient method for solving largescale problem (1), whose most important property is that its generated direction is always a sufficient descent direction for the objective function. Moreover, this property is independent of the line search used, and it reduces to the classical PRP method when the exact line search is used. The iterative process of the SDPRP method is given by where is the current iterate, is called the stepsize which can be obtained by some line search techniques, such as the Armijo line search, the Goldstein line search, and the (strong) Wolfe line search, and is the search direction determined by with where . It is easy to deduce from (3) and (4) that which indicates that is a sufficient descent direction of at the current iterate if ; that is, is not a stationary point of the objective function . It has been proved that SDPRP method has global convergence under an Armijotype line search [7] in the sense that which means that at least one cluster point of the sequence is a stationary point if it is bounded.
In another recent paper, Shi and Shen [8] showed that the classical PRP method in [1] has strong convergence and linear convergence rate under a customized Armijotype line search, which is somewhat complicated. The new Armijotype line search ensures that the search direction generated by the classical PRP method possesses the sufficient descent property, which is helpful to prove the global convergence.
In this paper, motivated by the Armijotype line search in [8], we first propose a similar but simple line search, which can ensure that the SDPRP method has strongly global convergence in the sense that that is, any cluster point of the sequence is a stationary point of the objective function . Noting that the above new line search needs to estimate the Lipschitz constant, which is not easy even for linear function, we present another Armijotype line search, which is motivated by the line search in [7]. This new line search can also guarantee the global convergence of the SDPRP method in the above sense.
The remainder of the paper is organized as follows. In Section 2 we introduce the two new Armijotype line searches and present the strongly convergent SDPRP method. The global convergence is established under the above two new Armijotype line searches in Section 3. Some numerical results are presented in Section 4, and in the last section, we conclude the paper with some remarks.
2. Strongly Convergent SDPRP Method
First, we give the following basic assumptions on the objection function .
Assumption 1.
Consider the following.The objective function has a lower bound on the level set , where is the starting point.In some neighborhood of , the gradient is Lipschitz continuous on an open convex set that contains , that is, there exists a constant such that
The level set is bounded.
Although is Lipschitz continuous, the Lipschitz constant is usually unknown in practice, even for the linear function . Therefore, we need to estimate the Lipschitz constant . Here, we adopt one of three estimating approaches proposed in [9]. More specifically, if , then we can set
with , and .
ArmijoType Line Search I. Set , , , and the initial stepsize , where is determined by (9). Let be the largest in such that
ArmijoType Line Search II. Set , . Let be the largest in such that
Now we begin to describe the strongly convergent SDPRP method.
Algorithm 2 [strongly convergent SDPRP method]
Step 0. Given an initial point , and , and set , .
Step 1. If then stop; otherwise go to Step 2.
Step 2. Compute the descent direction by (3) and (4). Determine the stepsize by the Armijotype line search (10) or (11).
Step 3. Set , and ; go to Step 1.
Lemma 3. Assume that (H1) and (H2) hold, then there exist and such that for any , one has where is defined by (9).
Proof. See [9, Lemma ].
Lemma 4. Assume that (H1) and (H2) hold. If , then the new Armijotype line search I is welldefined for the index .
Proof. The proof is easy; for completeness, we give the proof here. In fact, we can prove this lemma by contradiction. Suppose that the conclusion does not hold; then for , the inequality (10) does not hold for any nonnegative integer ; that is, Thus, Letting , by the continuity of and , we can obtain This and yield that which contradicts to . The proof is completed.
Lemma 5. Assume that (H2) and (H3) hold. If , then the new Armijotype line search II is welldefined for the index .
Proof. The lemma is also proved by contradiction. Suppose that the conclusion does not hold; then for , the inequality (11) does not hold for any nonnegative integer ; that is, That is, Letting , by the continuity of and , we can obtain that is, which contradicts to . The proof is completed.
3. Strongly Global Convergence
Throughout this section, we assume that , for all ; otherwise a stationary point of the objective function has been found.
3.1. Global Convergence of SDPRP Method with the Line Search I
We first prove the global convergent of SDPRP method with the Armijotype line search I.
Lemma 6. For all , one has where is defined in Lemma 3.
Proof. If then If then, from (3), (4), and (H2), we can get that which together with the triangular inequality implies that This completes the proof.
The following lemma shows that the stepsize sequence generated by the Armijotype line search I is bounded from below.
Lemma 7. For all , there exists a constant , such that in which is generated by the Armijotype line search I.
Proof. We divide the proof into two cases: and . For the first case, by (12) and (21), we get For the second case, that is , which indicates that does not satisfy (10); that is, Using the mean value theorem in the above inequality, we obtain , such that This inequality and (H2), (21) show that Therefore, we have that Obviously, (26) and (30) show that (25) holds with This completes the proof.
We are now ready to establish the strong convergence of SDPRP method using the Armijotype line search I.
Theorem 8. Suppose that (H1) and (H2) hold. Then
Proof. Since the generated sequence and the objection function is bounded below on the level set , by (10) and (25), we have Thus This completes the proof.
3.2. Global Convergence of SDPRP Method with the Line Search II
Then, we prove the strongly global convergent of SDPRP method with the Armijotype line search II. It is obvious that for all . Therefore, from the line search II, we have This together with (5) implies that In addition, (H3) implies that there is a constant such that
Lemma 9. Suppose that (H2) and (H3) hold. Then for all , one has
Proof. If , then does not satisfy (11); that is From the mean value theorem and (H2), there exists a constant , such that which together with (36) shows that (35) holds. This completes the proof.
We are now ready to establish the strong convergence of SDPRP method using the Armijotype line search II. The proof is motivated by the proof of Theorem 2.2 in [10].
Theorem 10. Suppose that (H2) and (H3) hold. Then
Proof. For the sake of contradiction, we suppose that the conclusion is not right. Then there exist a constant and an infinite index set such that Moreover, the fact , (35) and (H2) imply that This and (42) indicate that there exists a positive constant such that for sufficiently large , we have Then by (36) and (44), we can get By (3), (4), and (H2), for all , we have From (35), for all sufficiently large , there exist a constant , such that Therefore with . Thus, for , this and (37) imply that Thus, from (38) and (48), we get which contradicts (45). The proof is then completed.
4. Numerical Results
In this section, we present some numerical results to compare the performance of SDPRP method with the two new Armijotype line searches I and II and the threeterm PRP method in [7].(i)SDPRPI: the SDPRP method with the line search (10), with ;(ii)SDPRPII: the SDPRP method with the line search (11), with .(iii)TTPRP: the twoterm PRP method with the following Armijotype line search: let be the largest in such that where .
All codes were written in Matlab 7.1 and run on a portable computer. We stopped the iteration if the number of iteration exceeds 10000 or . Tables 1 and 2 list the numerical results for solving some test problems numbered from 1 to 30 in [11] with different dimension . Our numerical results are listed in the form NI/NF/CPU, where the symbols NI, NF, and CPU mean the number of iterations, the number of function evaluations, and the CPU time in seconds, respectively.


Figures 1 and 2 show the performance of these methods relative to the number of function evaluations and CPU time, respectively, which are evaluated using the profiles of Dolan and Moré [12]. That is, for each method, we plot the fraction of problems for which the method is within a factor of the best time. The left side of the figure gives the percentage of the test problems for which a method is fastest; while the right side gives the percentage of the test problems that are successfully solved by each of the methods. The top curve is the method that solved most problems in a time that was within a factor of the best time. Figures 1 and 2 show that SDPRPI method performs a little better than TTPRP method and obviously better than SDPRPII method. It solves about and of the problems with the smallest number of function evaluations and CPU time, respectively. Obviously, the performance of SDPRPII method is not so good, and, in the future, we will further study the corresponding line search. Of course, more numerical experiments should be carried out to test our proposed methods.
5. Conclusion
In this paper, we have proposed two new Armijotype line searches and proved that the sufficient descent PRP method proposed by Zhang et al. is strongly global convergent with the two new line searches. Numerical results show that the SDPRP method with the proposed line searches is efficient for the test problems.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors would like to thank the referees for giving them many valuable suggestions and comments, which improved this paper greatly. This work is supported by the Nature Science Foundation of Shandong Province (ZR2012AL08).
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Copyright
Copyright © 2014 Min Sun and Jing Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.