## Nonlinear Analysis: Optimization Methods, Convergence Theory, and Applications

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Qingjie Hu, Yu Chen, Zhibin Zhu, Bishan Zhang, "Notes on Convergence Properties for a Smoothing-Regularization Approach to Mathematical Programs with Vanishing Constraints", *Abstract and Applied Analysis*, vol. 2014, Article ID 715015, 7 pages, 2014. https://doi.org/10.1155/2014/715015

# Notes on Convergence Properties for a Smoothing-Regularization Approach to Mathematical Programs with Vanishing Constraints

**Academic Editor:**Yunhai Xiao

#### Abstract

We give some improved convergence results about the smoothing-regularization approach to mathematical programs with vanishing constraints (MPVC for short), which is proposed in Achtziger et al. (2013). We show that the Mangasarian-Fromovitz constraints qualification for the smoothing-regularization problem still holds under the VC-MFCQ (see Definition 5) which is weaker than the VC-LICQ (see Definition 7) and the condition of asymptotic nondegeneracy. We also analyze the convergence behavior of the smoothing-regularization method and prove that any accumulation point of a sequence of stationary points for the smoothing-regularization problem is still strongly-stationary under the VC-MFCQ and the condition of asymptotic nondegeneracy.

#### 1. Introduction

We consider the following mathematical program with vanishing constraints: where and are all continuously differentiable functions.

The MPVC was firstly introduced to the mathematical community in [1]. It plays an important role in some fields such as optimization topology design problems in mechanical structures [1] and robot path-finding problems with logic communication constraints in robot motion planning [2]. The major difficulty in solving problem (1) is that it does not satisfy some standard constraint qualifications at the feasible points so that the standard optimization methods are likely to fail for this problem. The MPVC has attracted much attention in the recent years. Several theoretical properties and different numerical approaches for MPVC can be found in [1–12]. Very recently, in [3], the authors have proposed a smoothing-regularization approach to mathematical programs with vanishing constraints. Their basic idea is to reformulate the characteristic constraints of the MPVC via a nonsmooth function and to eventually smooth it and regularize the feasible set with the aid of a certain smoothing and regularization parameter such that the resulting problem is more tractable and coincides with the original program for . Under the VC-LICQ and the condition of asymptotic nondegeneracy, the convergence behaviors of a sequence of stationary points of the smoothing-regularized problems have been investigated.

In this note, we give some improved convergence results about the smoothing-regularization approach to mathematical programs with vanishing constraints in [3]. We show that these properties still hold under the weaker VC-MFCQ and the condition of asymptotic nondegeneracy. The smoothing-regularization problems satisfy the standard MFCQ, which guarantees the existence of Lagrange multipliers at local minima; the sequence of multipliers is bounded, and the limit point is still strongly-stationary.

The rest of the note is organized as follows. In Section 2, we review some concepts of the nonlinear programming and the MPVC and present the smoothing-regularization method for (1), which is proposed in [3]. In Section 3, we give the improved convergent properties. We close with some final remarks in Section 4.

For convenience of discussion, some notations to be used in this paper are given. The th component of will be denoted by ; denotes the feasible set of problem (1). For a function and a given vector , we use and to denote the active index set of at and the support of , respectively.

#### 2. Preliminaries

Firstly, we will introduce some definitions about the following optimization problem: where are all continuously differentiable functions. denotes the feasible set of problem (2).

*Definition 1. *A point is called a stationary point if there are multipliers such that is a KKT point of (2); that is, the multipliers satisfy and with for all , and

*Definition 2. *A feasible point of (2) is said to satisfy the Mangasarian-Fromovitz constraint qualification (MFCQ for short) if the gradients are linearly independent and there is a such that

*Definition 3 (see [13]). *A finite set of vectors is said to be positive-linearly dependent if there exists such that

If the above system only has a solution , we say that these vectors are positive-linearly independent.

By using Motzkin’s theorem of the alternatives in [14], we can obtain the following property.

Lemma 4. *A point satisfies the MFCQ if and only if the gradients
**
are positive-linearly independent.*

Now, we borrow notations from mathematical programs with complementarity constraints to define the following sets of active constraints in an arbitrary as follows:

*Definition 5 (see [1]). *A feasible point for (1) satisfies the vanishing constraints Mangasarian-Fromovitz constraints qualification (VC-MFCQ for short) if
are linearly independent and there exists a vector such that

Similar to Lemma 4, we can also deduce the following result.

Lemma 6. *A point satisfies the VC-MFCQ if and only if the gradients
**
are positive-linearly independent. In other words, the MPVC at satisfies the VC-MFCQ if and only if there does not exist a vector with for all , for all , and for all such that
**
holds true.*

*Definition 7 (see [1]). *A feasible point for (1) satisfies the vanishing linear independence constraints qualification (VC-LICQ for short) if and only if
are linearly independent.

*Remark 8. *It is easy to see that the VC-LICQ implies the VC-MFCQ. Moreover, the VC-LICQ (VC-MFCQ) is weaker than the MPVC-LICQ (MPVC-MFCQ) (See [7]).

*Definition 9. *Let be a feasible point for the problem (1), then(a) is said to be weak-stationary if there exist multiplier vectors , and such that
(b) is said to be strongly-stationary, if it is weak-stationary and

Finally, we give the smoothing-regularization method of Problem (1), which is proposed in [3]. According to [3], with the help of a positive parameter, the MPVC (1) is approximated by the following smoothing-regularization problem: where In order to give our improved convergence analysis, the following concept of asymptotic nondegeneracy is necessary.

*Definition 10 (see [3]). *Let be feasible for the MPVC (1). Then a sequence of feasible points for (15) converging to as is called asymptotically nondegenerate, if any accumulation point of is different from 0 for each .

#### 3. Some Improved Convergence Properties

In this section, we will consider the improved convergence properties of a sequence of stationary points for the smoothing-regularization problem (15). Firstly, we discuss the constraint qualification of (15).

For convenience of discussion, we give the following notations: To show that the Mangasarian-Fromovitz constraints qualification for the problem (15) holds under some conditions, the following lemma palys a very important role.

Lemma 11. *Let be feasible for (1) such that the VC-MFCQ is satisfied at and the sequence of feasible points for (15) converging to as is asymptotically nondegenerate. Then, for sufficiently large , the set of vectors
**
are positive-linearly independent.*

*Proof. *Since are all continuous, for sufficiently large , we have
Because the VC-MFCQ holds, the gradients
are positive-linearly independent by Lemma 6, taking into account that
In view of the condition of asymptotic nondegeneracy, we know that for all and for for all sufficiently large . Similar to the proof of Proposition 2.2 in [15], we know that the set of vectors
are positive-linearly independent for all sufficiently large . The proof is completed.

Based on the above lemma, we can show the following theorem.

Theorem 12. *Let be feasible for (1) such that the VC-MFCQ is satisfied at and the sequence of feasible points for (15) converging to as is asymptotically nondegenerate. Then, for sufficiently large , Problem (15) satisfies the standard MFCQ at .*

*Proof. *Taking Lemma 11 into account, we know that the set of vectors
are positive-linearly independent for sufficiently large .

We now prove that the standard MFCQ holds at for Problem (15) for sufficiently large . In view of Lemma 4, we have to show that
with and holds for the zero vector. To see this, we rewrite (24) as
In view of the condition of asymptotic nondegeneracy, applying the positive linear independence of vectors from (23) to (25) and (19), one gets
The proof is completed.

*Remark 13. *In Theorem 12, by relaxing the condition of the VC-LICQ, we show that the VC-MFCQ and the condition of asymptotic nondegeneracy imply that the smoothing-regularization problems satisfy the standard MFCQ. Hence, Theorem 12 is an improved version of Lemma 5.6 in [3].

To establish the relations between the solutions of the original problem and those of the smoothing-regularization problem under the VC-MFCQ and the condition of asymptotic nondegeneracy, we give the following key lemma.

Lemma 14. *Let be convergent to zero. Suppose that is a sequence of stationary points of Problem (15) with and being the corresponding multiplier vectors. If is an accumulation point of the sequence such that the VC-MFCQ holds at and the condition of asymptotic nondegeneracy for is satisfied, then the sequence of multipliers is bounded.*

*Proof. *It follows from Theorem 12 that, for sufficiently large , there exist lagrangian multiplier vectors , , such that
From (27), we have
We can define
Noting that with , , and , (27) can be rewritten as
The following objective is to prove that the sequence is bounded.

Assume that the sequence is unbounded. Then, there exists a subset such that
So the corresponding normed sequence converges:
Combined with (31), it yields
that is,
where and, for all being large enough,
We can prove that . Actually, if , then, for at least one , . Without loss of generality, assume that for an , then, for all sufficiently large, . Consequently, for those . Taking into account the condition of asymptotic nondegeneracy, for , we have
which contradicts the assumption .

By Lemma 6, we know that contradicts the fact that the VC-MFCQ holds at . Thus, the sequence is bounded.

Again, noting the condition of asymptotic nondegeneracy and the definitions of , we can prove that the sequence of multipliers are bounded. The proof is completed.

Based on Lemma 14, similar to the proof of Theorem 5.3 in [3], we can obtain the following convergence result.

Theorem 15. *Let be convergent to zero. Suppose that is a sequence of stationary points of Problem (15) with . If is an accumulation point of the sequence such that the VC-MFCQ holds at and the condition of asymptotic nondegeneracy for is satisfied, then is a strongly-stationary point of Problem (1).*

*Remark 16. *In Theorem 15, by replacing the condition of the VC-LICQ, we prove that any accumulation point of stationary points for the smoothing-regularization problem is still strongly-stationary under the VC-MFCQ and the condition of asymptotic nondegeneracy. Hence, Theorem 15 includes Theorem 5.3 in [3] as a special case.

#### 4. Concluding Remarks

In this note, we have shown that the VC-LICQ assumption can be replaced by the weaker VC-MFCQ condition in order to get the strong stationarity for the smoothing-regularization approach to mathematical programs with vanishing constraints, which is proposed in [3]. We have also shown that the VC-MFCQ implies that the smoothing-regularization problems satisfy the standard MFCQ. While it seems possible to prove that many other VC-tailored constraint qualifications imply that the corresponding standard constraint qualification holds for the smoothing-regularization problem, it is an open question whether one can further relax the VC-MFCQ assumption to get strong stationarity in the limit.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This work is supported by NNSF (nos. 11371073, 11461015, 11361018) of China, Guangxi Natural Science Foundation (no. 2014GXNSFFA118001), and Guangxi Fund for Distinguished Young Scholars (no. 2012GXSFFA060003).

#### References

- W. Achtziger and C. Kanzow, “Mathematical programs with vanishing constraints: optimality conditions and constraint qualifications,”
*Mathematical Programming*, vol. 114, no. 1, pp. 69–99, 2008. View at: Publisher Site | Google Scholar | MathSciNet - C. Kirches, A. Potschka, H. G. Bock, and S. Sager, “A parametric active set method for quadratic programs with vanishing constraints,”
*Technical Report*, Interdisciplinary Center for Scientic Computing, University of Heidelberg, Heidelberg, Germany, 2012. View at: Google Scholar - W. Achtziger, T. Hoheisel, and C. Kanzow, “A smoothing-regularization approach to mathematical programs with vanishing constraints,”
*Computational Optimization and Applications*, vol. 55, no. 3, pp. 733–767, 2013. View at: Publisher Site | Google Scholar | MathSciNet - W. Achtziger, C. Kanzow, and T. Hoheisel, “On a relaxation method for mathematical programs with vanishing constraints,”
*GAMM-Mitteilungen*, vol. 35, no. 2, pp. 110–130, 2012. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - D. Dorsch, V. Shikhman, and O. Stein, “Mathematical programs with vanishing constraints: critical point theory,”
*Journal of Global Optimization*, vol. 52, no. 3, pp. 591–605, 2012. View at: Publisher Site | Google Scholar | MathSciNet - T. Hoheisel and C. Kanzow, “On the Abadie and Guignard constraint qualifications for mathematical programmes with vanishing constraints,”
*Optimization*, vol. 58, no. 4, pp. 431–448, 2009. View at: Publisher Site | Google Scholar | MathSciNet - T. Hoheisel and C. Kanzow, “Stationary conditions for mathematical programs with vanishing constraints using weak constraint qualifications,”
*Journal of Mathematical Analysis and Applications*, vol. 337, no. 1, pp. 292–310, 2008. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - T. Hoheisel and C. Kanzow, “First- and second-order optimality conditions for mathematical programs with vanishing constraints,”
*Applications of Mathematics*, vol. 52, no. 6, pp. 495–514, 2007. View at: Publisher Site | Google Scholar | MathSciNet - T. Hoheisel, C. Kanzow, and J. V. Outrata, “Exact penalty results for mathematical programs with vanishing constraints,”
*Nonlinear Analysis: Theory, Methods & Applications*, vol. 72, no. 5, pp. 2514–2526, 2010. View at: Publisher Site | Google Scholar | MathSciNet - T. Hoheisel, C. Kanzow, and A. Schwartz, “Convergence of a local regularization approach for mathematical programmes with complementarity or vanishing constraints,”
*Optimization Methods & Software*, vol. 27, no. 3, pp. 483–512, 2012. View at: Publisher Site | Google Scholar | MathSciNet - A. F. Izmailov and A. L. Pogosyan, “Optimality conditions and Newton-type methods for mathematical programs with vanishing constraints,”
*Computational Mathematics and Mathematical Physics*, vol. 49, no. 7, pp. 1128–1140, 2009. View at: Publisher Site | Google Scholar - A. F. Izmailov and M. V. Solodov, “Mathematical programs with vanishing constraints: optimality conditions, sensitivity, and a relaxation method,”
*Journal of Optimization Theory and Applications*, vol. 142, no. 3, pp. 501–532, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - C. Davis, “Theory of positive linear dependence,”
*The American Journal of Mathematics*, vol. 76, no. 4, pp. 733–746, 1954. View at: Publisher Site | Google Scholar | MathSciNet - O. L. Mangasarian,
*Nonliear Programming*, McGraw-Hill, New York, NY, USA, 1969, reprinted by SIAM, Philadelphia, Pa, USA, 1994. - L. Qi and Z. Wei, “On the constant positive linear dependence condition and its application to SQP methods,”
*SIAM Journal on Optimization*, vol. 10, no. 4, pp. 963–981, 2000. View at: Publisher Site | Google Scholar | MathSciNet

#### Copyright

Copyright © 2014 Qingjie Hu et al. 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.