Research Article  Open Access
A Noninterior Path following Algorithm for Solving a Class of Multiobjective Programming Problems
Abstract
Multiobjective programming problems have been widely applied to various engineering areas which include optimal design of an automotive engine, economics, and military strategies. In this paper, we propose a noninterior path following algorithm to solve a class of multiobjective programming problems. Under suitable conditions, a smooth path will be proven to exist. This can give a constructive proof of the existence of solutions and lead to an implementable globally convergent algorithm. Several numerical examples are given to illustrate the results of this paper.
1. Introduction
In this paper, the following conventions will be used. If , then if and only if ; if and only if ; if and only if , with strict inequality holding for at least one ; if and only if .
Multiobjective programming problems have been widely applied to various engineering areas which include optimal design of an automotive engine, economics, and military strategies. Consider the following multiobjective program (MOP): where , , and are assumed to be three times continuously differentiable. In this paper, the nonnegative and positive orthants of are denoted as and , respectively.
In the literature, solutions for a multiobjective programming problem are referred to variously as efficient, Paretooptimal, and nondominated solutions. In this paper we will refer to a solution of a multiobjective programming problem as an efficient solution. It is wellknown that if is an efficient solution of MOP problems, under some constraint qualifications (the Kuhn and Tucker constraint qualification [1] or the Abadie constraint qualification [2]), then the following KarushKuhnTucker (KKT) condition at for MOP problems holds [3, 4]: where , , , , , , and . We say that is a KKT point of MOP problems if it satisfies the KKT condition.
To solve linear programming, in 1984, Karmarkar [5] proposed a projective scaling algorithm, which is the first efficient polynomialtime algorithm in practice and hence competitive with the widely used simplex algorithm, which has no polynomiality although it is also efficient for linear programming. It was noted that Karmarkar’s projective scaling algorithm is equivalent to a projected Newton barrier algorithm [6]. Based on Karmarkar’s projective scaling algorithm, ones developed various central path following algorithms (or, in other terms, interior point methods and homotopy methods, see [7–12], etc.), which replaced projective scaling transformation of Karmarkar’s algorithm with affine scaling transformation. This modification can relax the particular assumptions on the simplex structure by Karmarkar’s algorithm. Later, the central path following algorithms were extended to solve convex nonlinear programming problems (see [13–16], etc.). It should be pointed out that all these central path following algorithms are globally convergent, but their global convergence results were obtained under the assumptions that the logarithmic barrier function is strictly convex and the solution set is nonempty and bounded.
Since Kellogg et al. [17] and Smale [18] proposed the notable homotopy method, this method has become a powerful solution tool with global convergence in finding solutions for various nonlinear problems, for example, zeros or fixed points of maps; see [19–24] and so forth. Furthermore, in [25], for convex nonlinear programming problems, by using the ideas of homotopy methods, Lin et al. proposed a new interior point method, which is called the combined homotopy interior point (CHIP) method, for solving convex programming problems. In that paper, the authors removed the convexity condition of the logarithmic barrier function and the nonemptiness of the solution set. In [26], by taking a piecewise technique, under the commonly used conditions in the literature, Yu et al. obtained the polynomiality of the CHIP method. Their results show that the efficiency of the CHIP method is also very well. The advantages mentioned above attract more and more researchers’ attention and the CHIP method has been applied to various areas such as fixed point problems [27, 28], variational inequalities [29, 30], and bilevel programming problems [31]. Furthermore, in 2008, for a class of nonconvex MOP problems, Song and Yao developed a new CHIP method [32]. In that paper, the authors constructed a new combined homotopy and thus obtained the existence of an interior path from a known interior point to a KKT point of (1).
It is wellknown that the choice of initial points plays an important role in the computational efficiency of the predictorcorrector algorithms (for a good introduction and a complete survey about the predictorcorrector algorithms, one can refer to the books [33, 34], etc.) resulting from the CHIP algorithm. Here it should be pointed out that, for parametric programming (see [35–42], etc., for related works), the predictorcorrector algorithms also had successful applications (see [35, 43], etc.). But in [32], initial points are generally confined in the interior of the feasible set, which is not easily localized for many cases; hence it is essential to enlarge the scope of choice of initial points. To this end, in this paper, we apply proper perturbations to the constraint functions and hence develop a noninterior path following algorithm. With the new approach, we are capable of choosing initial points more easily. This can improve the computational efficiency of the predictorcorrector algorithms greatly compared to before.
Another purpose of this paper is to solve MOP problems in a broader class of nonconvex sets than those in [32]. To complete this work, we introduce mappings and , which can make us extend the results in [32] to more general nonconvex sets.
In this paper, under the commonly used conditions in the literature, a bounded smooth homotopy path from a given initial point to a KKT point of (1) can be proven to exist. This forms the theoretical base of the noninterior path following algorithm. Numerically tracing the smooth path can lead to an implementable globally convergent algorithm for MOP problems. An explicit advantage of the noninterior path following algorithm is that the induced predictorcorrector algorithm has global convergence, compared with some locally convergent algorithms, for example, the notable Newton’s algorithms [33, 34]. Although the usual continuation methods (see [44–46], etc.) are globally convergent, they require that the partial derivative of the mapping in (9) with respect to is nonsingular. This requirement is often not easily satisfied in practice (see [33, 34], etc.). However, by the parameterized Sard theorem, the noninterior path following algorithm only requires that the mapping in (9) is of full row rank. This is another advantage of the algorithm presented in this paper. In addition, compared with the results in [32], we can solve MOP problems on more general nonconvex sets, and we also enlarge the scope of choice of initial points to the exterior of the feasible set.
This paper is organized as follows. In Section 2, we apply proper perturbations to the constrained functions, based on which, we construct a new combined homotopy and hence develop a noninterior path following algorithm. In Section 3, we use the predictorcorrector algorithm resulting from the noninterior path following algorithm to compute some experimental examples to illustrate the results of this paper. Finally, we make some conclusions in Section 4.
2. Theoretical Analysis of the Noninterior Path following Algorithm
In this section, let , , , , and . In addition, stands for the Euclidean norm.
In [32], Song and Yao developed a new CHIP method to solve the KKT point of (1) in a class of nonconvex sets; the main result of that paper is formulated as follows.
Theorem 1. Suppose that is nonempty and is bounded;for any the matrix is of full column rank;(the normal cone condition of ) for any the normal cone of at only meets at ; that is, for any , one has Then for almost all , there is a regular solution curve of the homotopy where , . When , the limit set is nonempty and the component of any point in is a KKT point of (1).
However, in [32], initial points are confined in the interior of . This point may reduce the computational efficiency of predictorcorrector algorithms greatly. To enlarge the scope of choice of initial points, in this paper, we apply proper perturbations to the constrained functions , and introduce the parameters Then let , , , − , , .
At the same time, to solve MOP problems in more general nonconvex sets, we assume that there exist continuous mappings and , such that the following assumptions hold. is nonempty and is bounded., ; besides, for any , if , then For any , if then , , , , .When , for any , we have
Next, we construct a new combined homotopy as follows: where , , and .
Remark 2. (1) In [32], the initial point is a strictly feasible point and has to satisfy the following constraints: and . However, it is not easy to choose such an initial point in practice when the constraint functions and are complex. For example, when the feasible set is − (see [25, Example 4.1]), it is difficult to choose the initial point satisfying all the constraints for this example. This difficulty may reduce the computational efficiency of the algorithm in [32]. In this paper, we can choose the initial point arbitrarily in , by selecting proper parameters and according to the signs of and . Since the initial point for the example mentioned above can be chosen arbitrarily in , this modification can improve the computational efficiency of algorithms greatly compared to before. In addition, compared with some locally convergent methods, for example, the notable Newton’s methods, the method proposed in this paper is a globally convergent method, whose initial points can be chosen more easily.
(2) In [32], the authors required that the feasible set must satisfy the socalled normal cone condition, which is a generalization of the convexity condition (Figure 1). If the feasible set is a convex set, then it satisfies the normal cone condition. On the other hand, if the feasible set satisfies the normal cone condition, then the outer normal cone of the feasible set at a boundary point can not meet the interior of the feasible set but meets the feasible set only at . In this paper, we extend the results in [32] to more general nonconvex sets. If the feasible set satisfies the normal cone condition, let , , , ; then it necessarily satisfies assumptions . Conversely, the conclusion does not hold. This point can be illustrated by Examples 1–4 in Section 3.
For a given , the zeropoint set of is denoted by
Lemma 3. Let be defined as in (9) and let assumptions hold. Then the equation has a unique solution.
Proof. When , the homotopy equation (9) becomes From the second and third equations in (11), we get that . Then assumption , together with the first equation in (11), yields that . By assumption , we get . So it follows from the third equation in (11) that . At last, from the fourth equation in (11), we obtain . Therefore (11) has a unique solution .
In the following, we recall some basic definitions and results from differential topology, which will be used in our main result of this paper.
The inverse image theorem (see [47]) tells us that if is a regular value of the map , then consists of some smooth curves. The regularity of can be obtained by the following lemma.
Lemma 4 (transversality theorem, see [21]). Let , , and be smooth manifolds with dimensions , and , respectively. Let be a submanifold of codimension (i.e., dimension of ). Consider a smooth map . If is transversal to , then, for almost all , is transversal to . Recall that a smooth map is transversal to if where is the Jacobi matrix of and and denote the tangent spaces of and at , respectively.
In this paper, , so the transversality theorem is corresponding to the parameterized Sard theorem on smooth manifolds.
Lemma 5 (parameterized Sard theorem). Let , be open sets and let be a map, where . If is a regular value of , then, for almost all , 0 is a regular value of .
With the preparation of the previous lemmas, we can prove the following main theorem on the existence and boundedness of a smooth path from a given point in to a KKT point of (1). This implies the global convergence of the noninterior path following algorithm.
Theorem 6. Let be defined as in (9) and let assumptions hold. Then, for almost all , there exists a curve of dimension 1 such that When , tends to a point . The component of is a KKT point of (1).
Proof . When , , and are considered as variables, we denote by . Let the Jacobian matrix of be denoted by , for any ,where . Since is a matrix of full row rank, is of full row rank. Therefore is also of full row rank, and 0 is a regular value of . By Lemma 5, for almost all , 0 is a regular value of map . By the inverse image theorem, consists of some smooth curves. Since , then a curve of dimension 1, denoted by , is starting from .
In the following, we furthermore assume that is nonsingular. By the classification theorem of onedimensional smooth manifolds, is diffeomorphic either to a unit circle or to a unit interval. For any , it is easy to show that is nonsingular; thus cannot be diffeomorphic to a unit circle. That is, is diffeomorphic to a unit interval.
Let be a limit point of ; then the following cases may occur: (a),(b),(c).
By Lemma 3, the equation has a unique solution in , so case (b) is impossible.
It follows from Theorem 3.2 in [32] that the projection of the smooth curve onto the plane is bounded.
Let and . If , from the first equation in (9), we have
By assumptions and , the fourth, fifth, and sixth parts in the lefthand side of (15) tend to infinity as , but the other three parts are bounded; this is impossible. Therefore the projection of the smooth curve onto the plane is also bounded.
If case (c) holds, then there exists a sequence of points such that . Since , , and are bounded, hence there exists a subsequence of points (denoted also by ) such that , , , , and as . From the second equation in (9), we have When , the active index set is When , the index set is (1) If , from the first equation in (9), we obtain By assumptions , , and (19), we have where . Therefore by (19) and (20), we get which contradicts assumption .
(2) If , from the first equation in (9), we conclude When , since and , are bounded, the righthand side of (22) is bounded. But by assumption , if , then the lefthand side of (22) is infinite. This results in a contradiction.
(3) If , since the nonempty index set , the proof is similar to (2).
By the above discussion, we obtain that case (a) is the only possible case, and thus the component of is a KKT point of (1).
For almost all , by Theorem 6, the homotopy generates a curve ; by differentiating the first equation in (13), we get the following theorem.
Theorem 7. The homotopy path is determined by the following initial value problem to the ordinary differential equation: where is the arclength of the curve .
Based on Theorems 6 and 7, various predictorcorrector procedures for numerically tracing the smooth homotopy path can be given (see [33, 34] and references therein).
3. Numerical Results
By using the homotopy (9) and the predictorcorrector algorithm, several numerical examples are given to illustrate the work in this paper. To illustrate that our result is an extension of the work in [32], we choose some examples whose feasible sets do not satisfy the normal cone condition but satisfy assumptions . In addition, the initial points are chosen not certainly to be in the interior of the feasible sets. In each example, we set , , and . The behaviors of homotopy paths are shown in Figures 2, 3, 4, and 5. Computational results are given in Table 1, where denotes the initial point, IT the number of iterations, the value of when the algorithm stops, and the KKT point.

Example 1 (adapted from [25, Example 3.1]). , , and are defined as in problem (1). The objective functions are given by The feasible set is given by Since , it is easy to see assumption is not satisfied at most points in . Therefore, the method presented in [32] can not be used to solve this example. However, if we introduce mappings , , and , then it is easy to verify that assumptions are satisfied in this example. Thus, we are able to solve this example via the algorithm presented in this paper. In addition, we choose two initial points arbitrarily which are not confined in the interior of the feasible sets; this is another improvement of the results in [32], which require that initial points should be confined in the interior of the feasible sets.
Example 2 (adapted from [48, problem 42]). , , and are defined as in problem (1). The objective functions are given by The feasible set is given by Since , it is easy to see assumption is not satisfied at most points in . Therefore, the method presented in [32] can not be used to solve this example. However, if we introduce mappings , , and , then it is easy to verify that assumptions are satisfied in this example. Thus, we are able to solve this example via the algorithm presented in this paper. In addition, we choose two initial points arbitrarily which are not confined in the interior of the feasible sets; this is another improvement of the results in [32], which require that initial points should be confined in the interior of the feasible sets.
Example 3 (adapted from [48, problem 56]). , , and are defined as in problem (1). The objective functions are given by The feasible set is given by Since , it is easy to see assumption is not satisfied at most points in . Therefore, the method presented in [32] can not be used to solve this example. However, if we introduce mappings , , and , then it is easy to verify that assumptions are satisfied in this example. Thus, we are able to solve this example via the algorithm presented in this paper. In addition, we choose two initial points arbitrarily which are not confined in the interior of the feasible sets; this is another improvement of the results in [32], which require that initial points should be confined in the interior of the feasible sets.
Example 4 (adapted from [48, problem 79]). , , and are defined as in problem (1). The objective functions are given by The feasible set is given by Since , it is easy to see assumption is not satisfied at most points in . Therefore, the method presented in [32] can not be used to solve this example. However, if we introduce mappings , , and , then it is easy to verify that assumptions are satisfied in this example. Thus, we are able to solve this example via the algorithm presented in this paper. In addition, we choose two initial points arbitrarily which are not confined in the interior of the feasible sets; this is another improvement of the results in [32], which require that initial points should be confined in the interior of the feasible sets.
Although the feasible sets of Examples 1–4 do not satisfy the normal cone condition in [32] and initial points are chosen not in the interior of the feasible sets, the numerical results for these examples can illustrate that the algorithm presented in this paper still works well.
4. Conclusions
In this paper, we apply proper perturbations to the constraint functions and hence develop a noninterior path following algorithm for solving a class of MOP problems. Our results extend the results in [32] to more general nonconvex sets and make initial points of the algorithm be chosen more easily than before. Since MOP problems have wide applications in engineering, management, economics, and so on, our results may be useful to propose a powerful solution tool for dealing with these practical problems. In the future, we want to propose new techniques to extend our results to more general nonconvex sets. In addition, we want to present a set of suitable unboundedness conditions to remove the boundedness assumptions on the feasible set so that we are able to solve MOP problems in unbounded sets.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors sincerely thank the editor and Professor Yong Li for the kind help provided. They are extremely grateful to the editor and the anonymous reviewer for their invaluable suggestions and helpful comments, which improved the paper greatly. This project was supported by NSFCUnion Science Foundation of Henan (no. U1304103), National Nature Science Foundation of China (no. 11201214), Natural Science Foundation of Henan Province (no. 122300410261), and Foundation of Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (no. 93K172012K07).
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