- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents

Abstract and Applied Analysis

Volume 2014 (2014), Article ID 542307, 5 pages

http://dx.doi.org/10.1155/2014/542307

## Constants within Error Estimates for Legendre-Galerkin Spectral Approximations of Control-Constrained Optimal Control Problems

Department of Mathematics, Linyi University, Shandong, China

Received 24 January 2014; Revised 24 March 2014; Accepted 27 March 2014; Published 28 April 2014

Academic Editor: Shuping He

Copyright © 2014 Jianwei Zhou. 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

Explicit formulae of constants within the a *posteriori* error estimate for optimal control problems are investigated with Legendre-Galerkin spectral methods. The constrained set is put on the control variable. For simpleness, one-dimensional bounded domain is taken. Meanwhile, the corresponding a *posteriori* error indicator is established with explicit constants.

#### 1. Introduction

Recently, spectral method has been extended to approximate the discretization of partial differential equations for design optimization, engineering design, and other engineering computations. It provides higher accurate approximations with a relatively small number of unknowns if the solution is smooth; see [1]. There have been extensive researches on finite element methods for optimal control problems, which focus on control-constrained problems; see [2–8]. The authors [9] studied state-constrained optimal control problems with finite element methods. However, there are few works on optimal control problems with spectral methods.

In order to get a numerical solution with acceptable accuracy, spectral methods only increase the degree of basis where the error indicator is larger than the* a posteriori* error indicator, while the finite element methods refine meshes (see [10]). There have been lots of papers concerning on* a posteriori* error estimates for -version finite element methods, but not for spectral methods. Guo [11] got a reliable and efficient error indicator for -version finite element method in one dimension with a certain weight. Zhou and Yang [12] deduced a simple error indicator for spectral Galerkin methods. In [13], the authors investigated Legendre-Galerkin spectral method for optimal control problems with integral constraint for state in one-dimensional bounded domain. It is difficult to obtain optimal* a posteriori* error estimates. Thus, if one gets the constants within upper bound* a posteriori* error estimates, it is easy to ensure the degree of polynomials to get an acceptable accuracy.

In this paper, the control-constrained optimal control problems are solved with Legendre-Galerkin spectral methods, and constants within upper bound of the* a posteriori* error indicator, which can be used to decide the least unknowns for acceptable accuracy, are proposed. By introducing auxiliary systems, explicit formulae of the constants within the* a posteriori* error estimates are obtained.

The outline of this paper is as follows. In Section 2, the model problem and its Legendre-Galerkin spectral approximations are listed. In Section 3, the constants within the* a posteriori* error estimates are investigated in details, and the explicit formulae are obtained. The conclusions are given in Section 4.

#### 2. A Model Problem and Its Legendre-Galerkin Spectral Approximations

Throughout this paper, we focus on and adopt the standard notations for Sobolev spaces with the norm and the seminorm ; see [14]. Specially, we set . If , we denote and by and , respectively.

The problem in which we are interest is the following distributed convex optimal control problem with integral constraint on the control variable: where , and the control variable , the state variable , and is the observation.

In order to assure existence and regularity of the solution, we assume that and are infinitely smooth functions; is a given positive constant, for simplicity, we set . It is well-known that (1) has a unique solution (see [5, 15]).

Now, we introduce the weak formula of (1). We give some basic notations which will be used in the sequel. Let Hence, the state equation (2) reduces to

Then, (1) can be rewritten as follows: find such that We recall following optimality conditions of the optimal control problem (for the details, please refer to [8, 15]): (1) has a unique solution . Meanwhile, is the solution of (1) if and only if there is a costate such that the triplet satisfies the following optimal conditions:

Let and let . One may expand the discrete polynomial spaces as One prefers to choose appropriate bases of such that the resulting linear system is as simple as possible. Following [16], we choose the basis functions as where denotes the -th degree Legendre polynomial. Then, Galerkin spectral approximations of (5) read as follows: find such that It is obvious that (9) has a solution and is the solution if and only if there is a costate satisfies the triplet such that Now, we are at the point to analyse the relationship between the optimal control and costate, which reads as follows: where denotes the integral average on of the costate (see [2]). Thus, for Galerkin spectral approximations, it follows that there holds Let It is clear that is uniformly convex. Then, there exits a independent of , such that

#### 3. Constants within the* a Posteriori* Error Estimates

In this section, we calculate all constants within the* a posteriori* error estimates. Firstly, we analyze the constant in Poincaré inequality.

For , we recall the Poincaré inequality with -norm as (see [17]) Now, we are at the point to investigate all of constants in details. We introduce an auxiliary state , which satisfies Subtracting (16) from (5), we get Let . It is clear that and then there hold which means that Hence, So, we can easily obtain that We denote by the constant in (22), and then Here, we recall the following orthogonal projection operator: for any , satisfies:

Lemma 1. *For all , one has
**
where .*

We denote by and two intermediate variables, and there hold Using (6), (10) and (14), for , we have which means that Now, we are at the point to derive the constant for . Let and . Then which is equivalent to Hence, where

Likewise, we derive the constant for . Similarly, let and . Then

We deduce that

Combining all of the above analyses, we derive that
which means that
For , there holds
where the* a posteriori* error indicator is defined as

#### 4. Conclusion

This paper discussed the explicit formulae of constants in the upper bound of the* a posteriori* error estimate for optimal control problems with Legendre-Galerkin spectral methods in one-dimensional bounded domain. Thus, with those formulae, it is easy to choose a suitable degree of polynomials to obtain acceptable accuracy. In the future, we are going to discuss the corresponding constants in the lower bound of the* a posteriori* error indicator.

#### 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 National Natural Science Foundation of China (no. 11201212), Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (no. BS2012DX004), AMEP, and the Special Funds for Doctoral Authorities of Linyi University.

#### References

- C. Canuto, M. Y. Hussaini, A. Quarteroni, and T. A. Zang,
*Spectral Methods in Fluid Dynamics*, Springer, New York, NY, USA, 1988. View at MathSciNet - Y. Chen, N. Yi, and W. Liu, “A Legendre-Galerkin spectral method for optimal control problems governed by elliptic equations,”
*SIAM Journal on Numerical Analysis*, vol. 46, no. 5, pp. 2254–2275, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - R. Ghanem and H. Sissaoui, “A posteriori error estimate by a spectral method of an elliptic optimal control problem,”
*Journal of Computational Mathematics and Optimization*, vol. 2, no. 2, pp. 111–125, 2006. View at Zentralblatt MATH · View at MathSciNet - R. Li, W. Liu, H. Ma, and T. Tang, “Adaptive finite element approximation for distributed elliptic optimal control problems,”
*SIAM Journal on Control and Optimization*, vol. 41, no. 5, pp. 1321–1349, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - W. B. Liu and N. Yan, “A posteriori error estimates for distributed convex optimal control problems,”
*Advances in Computational Mathematics*, vol. 15, no. 1–4, pp. 285–309, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - W. B. Liu and N. N. Yan, “A posteriori error estimates for convex boundary control problems,”
*SIAM Journal on Numerical Analysis*, vol. 39, no. 1, pp. 73–99, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - S. P. He and F. Liu, “Robust finite-time estimation of Markovian jumping systems with bounded transition probabilities,”
*Applied Mathematics and Computation*, vol. 222, pp. 297–306, 2013. View at Publisher · View at Google Scholar · View at MathSciNet - J. W. Zhou, Y. P. Chen, and Y. Q. Dai, “Superconvergence of triangular mixed finite elements for optimal control problems with an integral constraint,”
*Applied Mathematics and Computation*, vol. 217, no. 5, pp. 2057–2066, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - L. Yuan and D. P. Yang, “A posteriori error estimate of optimal control problem of PDE with integral constraint for state,”
*Journal of Computational Mathematics*, vol. 27, no. 4, pp. 525–542, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. Ainsworth and J. T. Oden, “A posteriori error estimation in finite element analysis,”
*Computer Methods in Applied Mechanics and Engineering*, vol. 142, no. 1-2, pp. 1–88, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - B. Q. Guo, “Recent progress in A-posteriori error estimation for the p-version of finite element method,” in
*Recent Advances in Adaptive*, Z. C. Shi, Z. Chen, T. Tang, and D. Yu, Eds., vol. 383 of*Comtemporary Mathematics*, pp. 47–61, American Mathematical Society, Providence, RI, USA, 2005. - J. W. Zhou and D. P. Yang, “An improved a posteriori error estimate for the Galerkin spectral method in one dimension,”
*Computers & Mathematics with Applications*, vol. 61, pp. 334–340, 2011. View at Publisher · View at Google Scholar - J. W. Zhou and D. P. Yang, “Spectral mixed Galerkin method for state constrained optimal control problem governed by the first bi-harmonic equation,”
*International Journal of Computer Mathematics*, vol. 88, no. 14, pp. 2988–3011, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - R. A. Adams,
*Sobolev Spaces*, Academic Press, New York, NY, USA, 1978. - J. L. Lions,
*Optimal Control of Systems Governed by Partial Differential Equations*, Springer, Berlin, Germany, 1971. View at MathSciNet - J. Shen, “Efficient spectral-Galerkin method. I. Direct solvers of second- and fourth-order equations using Legendre polynomials,”
*SIAM Journal on Scientific Computing*, vol. 15, no. 6, pp. 1489–1505, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - L. C. Evans,
*Partial Differential Equations*, vol. 19 of*Graduate Studies in Mathematics*, American Mathematical Society, Providence, RI, USA, 1998. View at MathSciNet