Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 5056148, 11 pages
https://doi.org/10.1155/2018/5056148

Research Article

A New Smoothing Method for Mathematical Programs with Complementarity Constraints Based on Logarithm-Exponential Function

Yu Chen¹ and Zhong Wan²

¹School of Mathematics and Statistics, Guangxi Normal University, 541004 Guilin, China
²School of Mathematics and Statistics, Central South University, 410083 Changsha, China

Correspondence should be addressed to Zhong Wan; wanmath@163.com

Received 6 February 2018; Accepted 14 June 2018; Published 12 August 2018

Academic Editor: Edoardo Artioli

Copyright © 2018 Yu Chen and Zhong Wan. 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.

Abstract.

We present a new smoothing method based on a logarithm-exponential function for mathematical program with complementarity constraints (MPCC). Different from the existing smoothing methods available in the literature, we construct an approximate smooth problem of MPCC by partly smoothing the complementarity constraints. With this new method, it is proved that the Mangasarian-Fromovitz constraint qualification holds for the approximate smooth problem. Convergence of the approximate solution sequence, generated by solving a series of smooth perturbed subproblems, is investigated. Under the weaker constraint qualification MPCC-Cone-Continuity Property, it is proved that any accumulation point of the approximate solution sequence is a M-stationary point of the original MPCC. Preliminary numerical results indicate that the developed algorithm based on the partly smoothing method is efficient, particularly in comparison with the other similar ones.

1. Introduction

Consider the following mathematical program with complementarity constraints (MPCC):

where , , , and , are all continuously differentiable functions. MPCC (1) stems from many fields, such as shape design, economic equilibrium, and multilevel game (see [1, 2]). In the past two decades, it has attracted great interest of research from applied mathematicians and engineers. One of the main challenges in studying such a problem is that the constraints in (1) may fail to satisfy some standard constraint qualification at a feasible point. Thus, many fundamental theoretical results and powerful algorithms for an ordinary smooth optimization problem cannot be directly employed to solve (1). Actually, some specific approaches have been proposed for solving MPCC (1), such as the sequential quadratic programming approach in [3–8], the interior point methods in [9, 10], the penalty approach in [11–13], the lifting method in [14], the relaxation approach in [15–22], and the smoothing methods in [23–30].

Among them, the smoothing method is one of the most popular approaches, which uses a smoothing function to approximate the complementarity constraints in (1). As a result, the original MPCC is reformulated into a standard smooth optimization model. Then, an approximate solution of MPCC is obtained by solving a series of smooth perturbed subproblems. Therefore, it is often necessary to prove theoretically that the sequence of approximate solutions converges to a stationary point (or an optimal solution) of the original MPCC.

Very recently, in [31], a partially smoothing Jacobian method is proposed for solving nonlinear complementarity problems with function. Numerical experiments have shown that this smoothing method outperforms the existent ones, particularly in comparison with the state-of-the-art methods derived from the classical Fischer Burmeister smoothing function and aggregation function.

Inspired by the idea of partly smoothing in [31], we intend to construct an approximate problem of (1) by partly smoothing the complementarity constraints in (1) such that the degree of approximation is improved between MPCC (1) and the constructed smooth problem. Specifically, in the existing results (see, for example, [23–30]), the complementarity constraints , , are often wholly approximated by a system of smooth equations with a perturbation parameter. In contrast, we construct an approximate problem of (1) only by replacing with a system of inequalities such that (1) is transformed into a standard smooth optimization problem. Consequently, under a weaker constraint qualification, called the MPCC-Cone-Continuity Property (MPCC-CCP), we can prove theoretically that any accumulation point of the approximate solution sequence is M-stationary to the original MPCC. Numerical experiments will be employed to show the efficiency of the proposed smoothing method, particularly in comparison with the other similar methods available in the literature.

The rest of the paper is organized as follows. In next section, we first review some concepts of nonlinear programming and MPCC; then we present a new smoothing method of Problem (1). Section 3 is devoted to establishment of convergence theory. In Section 4, numerical performance of the new method is reported. Final remarks are made in the last section.

Throughout this paper, represents the -th component of a vector and similar notations are used for vector-valued functions. denotes the feasible region of Problem (1). For a function and a given vector , stands for the active index set of at , i.e., for all . For a given vector , denotes the support set of .

2. Preliminaries and New Smoothing Approach

In this section, some basic concepts will be first stated, which are necessary to the development of a new smoothing method. Then, we will propose a new smoothing method of Problem (1).

A typical mathematical model of nonlinear programming (NLP) problems is expressed as follows:

where , , are all continuously differentiable functions.

Denote the feasible region of Problem (2). Stationary points in play a fundamental role in finding a minimizer of (2).

Definition 1 (see [32]). A point is called a stationary point of Problem (2) if there exist and such that is a KKT point of (2); that is to say

where and are called the vectors of multipliers.

Since it is usually not possible to solve the NLPs exactly, mostly of the standard NLPs method stops when the KKT conditions are satisfied approximately. Thus, approximate KKT point is very necessary.

Definition 2. A point is called an Approximate Karush-Kuhn-Tucker (AKKT) point of Problem (2) if there are sequences , and , where , such that

The well-known Karush-Kuhn-Tucker theorem declares that a local minimizer of Problem (2) is a stationary point if some constraint qualification holds. Linearly independent constraint qualification (LICQ) and Mangasarian-Fromovitz constraint qualification (MFCQ) are the most popular constraint qualifications.

Definition 3. A feasible point of (2) is said to satisfy the MFCQ if the gradients are linearly independent and there exists a vector such that

In [33], the above MFCQ is described by a concept of positive linearly dependent.

Definition 4 (see [33]). A finite set of vectors is said to be positive linearly dependent if there exists such that for all , , and

Conversely, if (6) holds if and only if , then the group of vectors is called to be positive linearly independent.

The following result has been proved in [33].

Lemma 5 (see [33]). A point satisfies the MFCQ if and only if the gradients

are positive-linearly independent.

Recently Andreani et al. [34] introduced a new CQ called Cone-Continuity Property (CCP) intimately related to the AKKT condition.

Definition 6 (see [34]). A feasible point of (2) is said to satisfy the CCP if the set-valued mapping such that

is outer semicontinuous (Definition 5.4 [35] )at ; that is,

It has been shown that CCP is strictly stronger than ACQ and weaker than CRSC in [34].

Next, we will extend the above concepts and results from NLP to MPCC. For an arbitrary feasible point of (1), we first define the following index sets.

Similar to Definitions 1 and 2, we give definitions of different stationary points for the MPCC.

Definition 7 (see [19]). Let be a feasible point of Problem (1). Then,
(a) is said to be W-stationary if there exist multiplier vectors and such that

(b) is said to be M-stationary, if it is W-stationary and

Definition 8 (see [21]). Let be a feasible point of Problem (1); is called a MPCC-AKKT point if there are sequences such that

where , either , or ,  .

A definition of MFCQ for MPCC is presented similar to Definition 3.

Definition 9 (see [19]). A feasible point of (1) is said to satisfy MPCC-MFCQ if and only if the vectors

are linearly independent and there exists a vector such that

The following result holds which is similar to Lemma 5.

Lemma 10 (see [19]). A feasible point of Problem (1) satisfies MPCC-MFCQ if and only if the gradients

are positive-linearly independent.

Andreani [21] extended the definition of CCP from nonlinear programming to MPCC.

Definition 11 (see [21]). A feasible point of (1) is said to satisfy MPCC-Cone-Continuity Property (MPCC-CCP) if and only if the set-valued mapping such that

is outer semicontinuous (Definition 5.4 [35] ) at ; that is,

In [21], it has been shown that MPCC-CCP is strictly weaker than MPCC-RCPLD and MPCC-CCP implies the MPCC-Abadie CQ under certain assumption. Furthermore, MPCC-CCP is also independent of MPCC-quasinormality and MPCC-pseudonormality. The following lemma shows the relationship between MPCC-CCP and MPCC-AKKT.

Lemma 12 (see [21]). MPCC-CCP holds at if and only if MPCC-AKKT point is an M-stationary point.

In the end of this section, we come to propose a new smoothing method of Problem (1).

We first note that

can be written as

Clearly, (23) is equivalent to

Since

we obtain an equivalent form of (22):

More generally, we set

where is continuously differentiable. Then, can be approximated by a logarithm-exponential function [36]:

The following result presents some nice properties of the logarithm-exponential function.

Lemma 13 (see [36]). Let be defined by (28). Suppose that , , are continuously differentiable. Then,
(a) is increasing with respect to , and .
(b) For any and , it holds that

On the basis of Lemma 13, we approximate by the following logarithm-exponential function:

Then, it is natural that for the following complementarity constraints

we can approximate in (31) by the following system of inequalities:

where is given by

Consequently, the original MPCC (1) is approximated by the following new smooth optimization problem:

We denote the feasible region of Problem (34) by . The Lagrange function of Problem (34) is as follows:

where .

Since Problem (34) is a standard smooth optimization problem, many powerful optimization techniques can be directly applied to solve it (see [37–40]).

Remark 14. Unlike the existing smoothing methods, it is noted that (34) is obtained only by partly smoothing the complementarity constraints (31).
As an approximate problem of the MPCC (1) with a perturbation parameter , a critical issue should be addressed that concerns what is the relation between the optimal solutions of (34) and (1). Therefore, our next focus in this paper is to prove theoretically that the solution of the perturbed problem (34) tends to an optimal solution of (1) as .

3. Convergence Analysis and Development of Algorithm

In this section, we will investigate the limiting behavior of stationary points of the perturbed subproblems.

We first study the constraint qualification of (34).

Lemma 15. Let be defined by (33). Then, (1) for all , is continuously differentiable and is decreasing with respect to .
(2) The gradient of is calculated by

where

(3) .
(4) Let be feasible for Problem (1). If , then , as and . If , then, , as and .

Proof. From the definition of , it is easy to see that the first result holds. By direct calculation, we obtain the second result. The third is directly from the second one. It remains to prove the last result.
As , we write and as

and

respectively. Thus, it is easy to see that , as and . In a similar way, we can prove that , (, ) in the case that .

In virtue of Lemmas 10 and 15, we now prove that Problem (34) satisfies some constraint qualification under mild conditions.

Theorem 16. Let be a feasible point of Problem (1) such that MPCC-MFCQ is satisfied at . Then, there exist a neighborhood of and a sufficiently small such that Problem (34) satisfies the standard MFCQ at any point for any .

Proof. Since are all continuous, there exist a neighborhood and a positive constant such that for any and any point , we have

For convenience of discussion, we denote

We will first prove that the following equalities

are true.
Actually, if , then and

Therefore, . It says that .
In a similar fashion, we can prove that .
Noting that MPCC-MFCQ is satisfied at for Problem (1), we conclude from Lemma 10 that the following gradients

are positive-linearly independent.
Owing to the facts

it follows from the result (4) in Lemma 15 that , ) and , ) as and .
Similar to the proof of Proposition 2.2 in [41], it is concluded that there are a neighborhood and a sufficiently small such that for all with , the vectors

are positive-linearly independent.
We now claim that if , then the standard MFCQ holds for Problem (34), where and .
Take . In view of Lemma 5, we should show that

if and only if all the multiplier vectors, , , , and are null ones. To see this, we rewrite (47) as

From (46) and (48), it follows that

Taking into account for all , it yields

Since ( and (, we know   (. Thus, (47) holds. The proof is completed.

The following result establishes the relations between the optimal solutions of the original problem and that of the perturbed subproblem under the constraint qualification of MPCC-CCP.

Theorem 17. Let be a positive sequence which is convergent to zero as . Suppose that is a sequence, generated by the stationary points of the smooth problem (34) with perturbation parameter . If is an accumulation point of the sequence and MPCC-CCP holds at and , then is an M-stationary point of the original MPCC (1).

Proof. From Lemma 12, it follows that we only need to show that is an MPCC-AKKT point. From Definition 8, it will be sufficient to show that there is subsequence which is an MPCC-AKKT point subsequence. Since is a stationary point sequence generated by the smooth problem (34) with perturbation parameter , there exist Lagrangian multiplier vectors , , and such that

and

Clearly, (51) can be rewritten as

Since

we can write

and

Then, (51) is equivalent to

First, we will show that for enough large k. Since , we decompose into four subsets.

Since , we can get that and . If , we can follow that . Because , we can conclude that . Furthermore, for the condition of Theorem 17 that , we can get that .
If , then and . Since , we can conclude that and . Since , then it is sure that . Thus, . Therefore, we can conclude that .
Following the similar arguments, we can conclude that . Lastly, we will prove that either , or for . Similarly we decompose into four subsets.

If , we can follow that and . Because and , we can conclude that and . Furthermore, the condition of Theorem 17 that , we can get that . Therefore, .
If , we can follow that and . Since and , we can conclude that and . Because , it is sure that . Thus, or 0, . Therefore, .
If , we can follow that and . Since and , we can conclude that and . Because , it is sure that . Thus, or 0, . Therefore, .
If , we can follow that and . Since and , we can conclude that and . Because and , thus, or 0, or 0, . Therefore, either , or ,.
Thus, we conclude that is an MPCC-AKKT point. Following the condition of Theorem 17 that MPCC-CCP holds at , we can get that is an M-stationary point.

On the basis of Theorems 16 and 17, we now develop an implementable algorithm to solve the original MPCC (1) before the end of this section.

Algorithm 18. Step 1. Given an initial point . Choose , , . Set .
Step 2. Let be the current parameter. Solve the following problem:

where

The optimal solution is referred to as .
Step 3. If , then the algorithm stops. Otherwise, set , , . Return to Step 2.

Remark 19. In Step 3 of Algorithm 18, denotes the maximal violation of all the constraints and is defined by

It is clear that can be used to measure the infeasibility degree at the obtained iterate point . If , then is a feasible point of MPCC (1), as well as a stationary point of the perturbed problem. From Theorems 17, it follows that is an approximate optimal solution of MPCC (1).

4. Numerical Results

In this section, we investigate the numerical behavior of Algorithm 18. We compare Algorithm 18 with a similar algorithm developed by Facchinei et al. in [23] as they are used to solve the same test problems. All the test problems are from [23, 42]. The solution tolerance is set to be for the problems 8(f)-8(j). For the other test problems, the solution tolerance takes .

As done in [23], the initial perturbation parameter is set to be 1, and for reduction of the perturbation parameter. The corresponding computer procedures in MATLAB run in the following computer environment: GHz CPU, GB memory based operation system of Windows 7.

Numerical efficiency of the two algorithms is reported in Tables 1 and 2. For each algorithm, the optimal value of the objective function, the optimal solution, the number of iterations, and the achieved termination condition are recorded for evaluating the numerical performance. The used notations in Tables 1 and 2 are listed as follows.

Table 1: Comparison between different algorithms.

Prob

1(a)
3.2077/3.2077
4.0604/4.0604
5/2
7.1071e-06/3.4786e-08

1(b)
3.2077/3.2077
4.0604/4.0604
5/2
7.1063e-06 /3.4786e-08

2(a)
3.4494/3.4494
5.1536/5.1536
5/2
2.0042e-08/8.3186e-08

2(b)
3.4494/3.4494
5.1536/5.1536
5/2
2.0042e-08/8.3741e-08

3(a)
4.6043/4.6043
2.3894/2.3894
5/2
3.5255e-08/5.6882e-07

3(b)
4.6043/4.6043
2.3894/2.3894
5/2
3.5255e-08/5.6882e-07

4(a)
6.5927/6.5927
1.3731/1.3731
5/3
5.2677e-08/1.1699e-14

4(b)
6.5927/6.5927
1.3731/1.3731
5/3
5.2677e-08/1.9621e-17

5
-1.0000/-1.0000
(0.50,0.50)/(0.50,0.50)
6/1
6.0954e-07/5.2779e-16

6
-3.2667e+03/-3.2667e+03
93.3333/93.3333
4/1
3.7500e-08/2.2901e-16

7
4.0753e-06/7.1054e-15
(0,30.0000)/(0,30.0000)
7/2
7.5895e-07/4.1748e-15

8(a)
-343.3453/-343.3453
55.5513/55.5513
4/1
4.6387e-08/3.7728e-09

8(b)
-203.1551/-203.1551
42.5382/42.5382
4/1
5.1908e-08/2.6567e-13

8(c)
-68.1356/-68.1356
24.1451/24.1451
4/1
6.6333e-08/6.9827e-15

8(d)
-19.1541 /-19.1541
12.3727/12.3727
4/1
8.6584e-08/4.8701e-12

8(e)
-3.1612/-3.1612
4.7536/4.7536
4/1
1.3410e-07/1.1826e-11

8(f)
-346.8932/-346.8932
50.0000/50.0000
5/2
4.4957e-08/6.7134e-04

8(g)
-224.0372/-224.0622
39.7914/39.7003
5/2
3.6489e-08/4.2135e-04

8(h)
-80.7860/-80.7861
24.2571/24.2562
5/2
1.8589e-08/2.0398e-05

8(i)
-22.8371/-22.8690
13.0197/12.9023
5/2
5.4911e-08/1.2813e-04

8(j)
-5.3491/ -5.3492
6.0023/6.0015
5/2
1.7337e-08/6.2099e-05

9(a)
1.4267e-12/4.7851e-06
(10.0,5.0)/(7.0226,7.9755)
5/1
1.6052e-08/1.6627e-13

9(b)
5.3783e-15/1.1248e-14
(10.0,5.0)/(5.0,9.0)
5/2
7.7636e-07/2.5611e-07

9(c)
1.2633e-14/1.8666e-10
(10.0,5.0)/(5.0,9.0)
5/3
1.6052e-08/8.6309e-17

9(d)
1.6456e-14/9.4146e-12
(10.0,5.0)/(5.0,9.0)
5/3
1.6051e-08/3.7030e-15

9(e)
1.4267e-12/9.4146e-12
(10.0,5.0)/(5.0,9.0)
5/3
1.6052e-08/3.5898e-15

10
-6.6000e+03/-6.6000e+03
(7.3019,3.4529,11.6981,17.5471) /(7.9142,4.3714,11.0858,16.6286)
4/1
2.5000e-08/2.7393e-15

11
-12.6787/-12.6787
(0.0,2.0)/(0.0,2.0)
5/3
1.4835e-08/1.08886e-15

Table 2: Comparison between different algorithms (continued).

Prob

bard1
16.8919 /16.8919
0.9459/0.9459
4/2
4.5868e-07/1.2435e-14

bard3
-12.6787/-12.6787
0.0000/0.0000
4/2
8.3540e-07/3.7267e-07

dempe
28.2500/28.4859
0.0000/0.0009
2/1
5.1436e-11/6.6730e-15

df1
5.3751e-13/0.0000
1.0000/1.0000
7/1
7.1965e-07/0.0000

ex9.1.2
-16.0000/-16.0000
4.0000/4.0000
4/3
4.1667e-07/7.6800e-17

jr1
0.5000/0.5000
0.5000 /0.5000
5/1
2.0000e-08/0.0000

jr2
0.5000/0.5000
0.5000/0.5000
2/3
2.0000e-12/0.0000

kth1
1.8097e-06 /0.0000
8.9720e-07/8.9722e-07
7/1
0.0000/0.0000

kth2
1.0000e-06/0.0000
0.0000/0.0000
4/1
0.0000/0.0000

kth3
0.5000/0.5000
0.0000/0.0000
5/3
1.0000e-08/3.1427e-17

ralph2
-2.0003e-12/7.0641e-14
1.0070e-06/7.9080e-07
7/7
9.9300e-07/1.8633e-07

scholtes1
2.0000/ 2.0000
0.0000/0.0000
5/1
2.0000e-08/1.5489e-16

scholtes2
15.0000/15.0000
0.0000/0.0000
7/1
8.3043e-07/2.4980e-18

scholtes3
0.5000/ 0.5000
1.0000/1.0000
5/3
1.0000e-08/7.8774e-18

scholtes4
-2.0000e-06/-1.3863e-06
1.0000e-06/6.9830e-07
7/7
9.9999e-07/6.9315e-07

scholtes5
1.5000 /1.0000
1.5000/1.0000
5/1
1.3333e-08/0.0000

Prob: the test problems;

: the optimal function value obtained by the algorithm in [23]/the optimal function value by Algorithm 18;

: the optimal solution obtained by the algorithm in [23]/the optimal solution obtained by Algorithm 18;

: the number of iterations as the algorithm in [23] stops/the number of iterations as Algorithm 18 stops;

: the maximal degree of constraint violation as the algorithm in [23] stops at the optimal solutions /the maximal degree of constraint violation as Algorithm 18 stops at the optimal solutions .

As done in [23], only the -part of the optimal solution is shown in Table 1. In Table 2, for the optimal solutions found by the algorithm in [23] and Algorithm 18, only the first component of is shown in .

From Tables 1 and 2, it is clear that (1) Algorithm 18 can obtain the same optimal function value for the other test problems as the algorithms in [23] except for the problem: Scholtes 5. For this test problem, our algorithm gets smaller (better) value of the objective function than the algorithm in [23]. (2) By the two algorithms, almost the same optimal solutions have been obtained for all the test problems. (3) As an impressive performance, Algorithm 18 costs smaller number of iterations with higher accuracy in finding out the optimal solution. Actually, for the 41 ones out of all the 44 test problems, the number of iterations is smaller than that of the algorithm in [23]. (4) With regard to the termination conditions, for the 33 ones out of the 44 test problems, Algorithm 18 has less degree of constraint violation at the optimal solution than that of the algorithm in [23].

The numerical results in Tables 1 and 2 demonstrate that Algorithm 18 outperforms the algorithms in [23], and the proposed partly smoothing method in this paper is promising in solving MPCC.

5. Final Remarks

Different from the existing smoothing methods available in the literature, we have proposed a partly smoothing method based on the logarithm-exponential function for the mathematical programs with complementarity constraints. It has been proved that Mangasarian-Fromovitz constraint qualification holds for the constructed approximate smooth problem in our method. Under the weaker constraint qualification MPCC-CCP, it has been proved that any accumulation point of the approximate solution sequence is an M-stationary point of the original MPCC. Preliminary numerical results have demonstrated that the proposed smoothing method is more efficient than the other similar ones.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research is supported by the National Natural Science Foundation of China Grant nos. 71671190, 11461015, and 11761014, Guangxi Natural Science Foundation Grant nos. 2015GXNSFAA139010 and 2017GXNSFAA198243, and Guangxi Colleges and Universities Key Laboratory of Mathematical and Statistical Model Grant no. 2016GXKLMS010.

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