Abstract
We develop a convergence analysis for the simplest finite element method for a model linear-quadratic elliptic distributed optimal control problem with pointwise control and state constraints under minimal assumptions on the constraint functions. We then derive the generalized Karush–Kuhn–Tucker conditions for the solution of the optimal control problem from the convergence results of the finite element method and the Karush–Kuhn–Tucker conditions for the solutions of the discrete problems.
1 Introduction
Let
where
Here
and
Remark 1.1.
We follow the standard notation for differential operators, function spaces and norms that can be found for example in [1, 8, 5].
Remark 1.2.
The inequalities between functions in
We will write the partial differential equation (PDE) constraint (1.2) as
where
From the elliptic regularity theory for polyhedral domains in [9, 12], we have
where
by the Sobolev embedding theorem (cf. [1]).
Consequently, the constraints in
(1.4) are well-defined and
We will consider the optimal control problem (1.1)–(1.4) under the Slater condition that
It follows from (1.9) that the closed convex subset
The generalized Karush–Kuhn–Tucker (KKT) conditions for
for all
The usual approach to the analysis of finite element methods for the
optimal control problem (1.1)–(1.4) is
to first establish the generalized KKT conditions (1.10)–(1.12)
(or their variants)
through the abstract theory for Lagrange multipliers in infinite-dimensional spaces
under the Slater condition
(cf. [13, 18, 22, 7, 14]),
and then to use the generalized KKT conditions together with additional regularity
assumptions on the constraint functions
In this paper we adopt the opposite approach by first establishing convergence results for the simplest finite element method, which approximates the state y (resp. control u) by continuous piecewise affine (resp., piecewise constant) functions, under the minimal assumptions (1.5) and (1.6) on the constraint functions and the Slater condition (1.9). The key is to use the Slater condition to generate appropriate feasible points for the discrete constrained optimization problems. As far as we know these convergence results have not appeared in the literature. We then use the convergence results together with the KKT conditions for the solutions of the discrete problems to obtain (1.10)–(1.12). This Galerkin approach is more elementary and it also makes the role played by the Slater condition more transparent.
The rest of the paper is organized as follows. The finite element method is introduced in Section 2 and analyzed in Section 3. The KKT conditions (1.10)–(1.12) are established in Section 4 and we end with some concluding remarks in Section 5. Appendix A contains details for the KKT conditions for the solution of the discrete problem.
Throughout the paper we use C with or without subscripts to denote a generic positive constant that may take different values at different occurrences.
2 A Finite Element Method
Let
2.1 The Discrete Problem
The discrete problem for (1.1)–(1.4) is to find
where
Here
We will write the discrete PDE constraint (2.2) as
where
Remark 2.1.
The operators
There are two trivial estimates
2.2 Finite Element Estimates
We have the following standard interpolation error estimates
for
that follow from the Bramble–Hilbert lemma (cf. [3]), scaling and (1.8).
Since
Let
The following estimates for
and
for all
The estimate (2.13) is an immediate consequence of (2.8) and Galerkin orthogonality, and the estimate (2.14) follows from (2.8), (2.9), (2.13) and the discrete Sobolev inequality
where
Remark 2.2.
The case of (2.15) for
2.3 Connection Between
(
-
Δ
)
-
1
and
(
-
Δ
j
)
-
1
The following result connecting
Lemma 2.3.
Let
In particular, the limits (2.16) and
(2.17) are valid for
Proof.
Note that (1.2),
(2.2) and
(2.12) imply that
follows immediately from (1.7) and (2.13).
Similarly, since
is an immediate consequence of (1.7) and (2.14).
From (1.7) we have
which implies (2.16) because of (2.18).
Similarly, we have, by (1.7) and (1.8),
Since
and then the Slater condition (1.9) and (2.11) imply
for j sufficiently large, where β is a positive constant.
Remark 2.4.
To facilitate the presentation,
we will assume (by renumbering if necessary) that the discrete Slater condition
(2.21) holds for
Since
Consequently, the closed
convex subset
2.4 KKT Conditions for
(
y
¯
j
,
u
¯
j
)
Since the constraints in
(2.2)–(2.4) are affine,
it follows from a well-known constraint qualification (cf. [2, CQ3 in Section 5.4])
that
there exist Lagrange multipliers
for all
Moreover, we have the sign conditions
and the complementarity conditions
Details for the KKT conditions (2.24)–(2.26) are provided in Appendix A.
Remark 2.5.
It follows from (2.24) that
for all
Remark 2.6.
The Lagrange multipliers
First we note that the Lagrange multiplier
that comes from letting
If we let
which is equivalent to
It follows from (2.25), (2.26) and (2.29) that
for all
We deduce from (2.6)
(cf. [15, Theorem 2.3 in Chapter I])
that
because the characteristic functions of distinct elements of
It follows from (2.32) that, for any
Consequently, if we define the canonical
then (2.30) (and hence (2.24)) and the sign conditions for
3 Convergence Analysis for
(
y
¯
j
,
u
¯
j
)
According to (1.7), (2.1), (2.5) and (2.22), we have
for
3.1 A Sequence of Feasible Points for the Discrete Problems
We can use the discrete Slater condition (2.21)
to construct a fundamental sequence of feasible points in
Lemma 3.1.
There exists a sequence of positive numbers
Proof.
It suffices to find
Since
In view of the constraints in (1.4) for
and hence
where
Let β be the constant in (2.21)
and
3.2 Reduced Optimization Problems
Let
Then
where the closed convex subset
Similarly, let
Then
where the closed convex subset
In particular, the sequence from Lemma 3.1 satisfies
Remark 3.2.
Let
where
The connection between
Lemma 3.3.
Let
If
Proof.
Let
The estimate (3.13) then follows from (1.7) and (2.18), and the estimate (3.14) follows from (1.7) and (2.16). ∎
3.3 Convergence Results
We are now ready to prove the convergence of
Theorem 3.4.
We have
Proof.
It suffices to show that any subsequence of
Let
Let
and
Let
Consequently, we have
The weak convergence of
because
and hence
Therefore
Putting (2.18), (3.1), (3.13), (3.16)–(3.18) and (3.20) together, we see that
which together with (3.16) implies
Finally, we find, from (3.12) and (3.21),
and then (2.10) implies
Combining Lemma 2.3 and Theorem 3.4, we arrive at the following corollary.
Corollary 3.5.
We have
4 Generalized KKT Conditions for
(
y
¯
,
u
¯
)
The key is to bound the Lagrange multipliers that appear in the KKT conditions (2.24)–(2.26) for the solutions of the discrete problems.
4.1 Bounds for
μ
j
,
1
and
μ
j
,
2
We begin with a bound for the measures
Lemma 4.1.
There exists a positive constant C such that
Proof.
Substituting
Note that
by (2.3), (2.25) and (2.26), and
4.2 A Bound for
p
j
Next we find a bound for the function
Lemma 4.2.
There exists a constant C such that
Proof.
Let
It follows from (1.7), (1.8), (3.1) and (4.1) that
the right-hand side of (4.6) is bounded by
Let
which implies
4.3 Bounds for
λ
j
,
1
and
λ
j
,
2
Now we establish the bounds for
Lemma 4.3.
There exists a positive constant C such that
4.4 Generalized KKT Conditions
By Lemma 4.1, Lemma 4.3 and
the weak compactness of bounded Borel measures and bounded functions in
In view of (2.10), (2.11), (2.26), (3.15), (3.23), the complementarity conditions in (1.12) follow from the limits
Let
Then we have, by (2.27),
It follows from (2.10) that
and then Lemma 2.3 yields
Therefore, in view of (3.22)
and (3.23), we
obtain (1.10) by letting
Remark 4.4.
Let the space
where
We can rewrite (1.10) as the following analog of (2.24):
for all
5 Concluding Remarks
There are only a few papers in the literature on finite element
methods for the model linear-quadratic elliptic distributed
optimal control problem with pointwise control and state constraints,
and the convergence analyses in these papers are based on stronger
assumptions on the constraint functions
that yield additional elliptic regularity for
The generalized KKT conditions (1.10)–(1.12) can be obtained by invoking the abstract existence results for Lagrange multipliers in infinite-dimensional spaces under the Slater condition (1.9) (cf. [13, 18, 22, 14]). In contrast, the Galerkin approach in this paper is based on the classical KKT conditions for the solutions of the finite-dimensional discrete problems, basic finite element estimates, basic techniques of the direct method in the calculus of variations, and real analysis. Therefore our approach is in some sense a more elementary approach. It also makes the role played by the Slater condition more transparent.
Finally, we note that it is possible to extend the approach in this paper to elliptic boundary optimal control problems (cf. [16, 7, 21]).
Funding source: National Science Foundation
Award Identifier / Grant number: DMS-22-08404
Funding statement: This work was supported in part by the National Science Foundation under Grant No. DMS-22-08404.
A KKT Conditions for the Solution of the Discrete Problem
Let
The minimization problem (2.1)–(2.4) can be rewritten as
subject to the constraints
where
and
Let
for all
and for
Using (A.3)–(A.7), we can
translate (A.8)–(A.10)
into (2.24)–(2.26) by taking
References
[1] R. A. Adams and J. J. F. Fournier, Sobolev Spaces, 2nd ed., Pure Appl. Math. (Amsterdam) 140, Elsevier/Academic, Amsterdam, 2003. Search in Google Scholar
[2] D. P. Bertsekas, A. Nedić and A. E. Ozdaglar, Convex Analysis and Optimization, Athena Scientific, Belmont, 2003. Search in Google Scholar
[3] J. H. Bramble and S. R. Hilbert, Estimation of linear functionals on Sobolev spaces with application to Fourier transforms and spline interpolation, SIAM J. Numer. Anal. 7 (1970), 112–124. 10.1137/0707006Search in Google Scholar
[4] S. C. Brenner, T. Gudi, K. Porwal and L.-Y. Sung, A Morley finite element method for an elliptic distributed optimal control problem with pointwise state and control constraints, ESAIM Control Optim. Calc. Var. 24 (2018), no. 3, 1181–1206. 10.1051/cocv/2017031Search in Google Scholar
[5] S. C. Brenner and L. R. Scott, The Mathematical Theory of Finite Element Methods, 3rd ed., Texts Appl. Math. 15, Springer, New York, 2008. 10.1007/978-0-387-75934-0Search in Google Scholar
[6]
S. C. Brenner, L.-Y. Sung and Z. Tan,
A cubic
[7] E. Casas, Boundary control of semilinear elliptic equations with pointwise state constraints, SIAM J. Control Optim. 31 (1993), no. 4, 993–1006. 10.1137/0331044Search in Google Scholar
[8] P. G. Ciarlet, The Finite Element Method for Elliptic Problems, Stud. Math. Appl. 4, North-Holland, Amsterdam, 1978. 10.1115/1.3424474Search in Google Scholar
[9] M. Dauge, Elliptic Boundary Value Problems on Corner Domains, Lecture Notes in Math. 1341, Springer, Berlin, 1988. 10.1007/BFb0086682Search in Google Scholar
[10] I. Ekeland and R. Témam, Convex Analysis and Variational Problems, Class. Appl. Math. 28, Society for Industrial and Applied Mathematics, Philadelphia, 1999. 10.1137/1.9781611971088Search in Google Scholar
[11] L. C. Evans and R. F. Gariepy, Measure Theory and Fine Properties of Functions, Stud. Adv. Math., CRC Press, Boca Raton, 1992. Search in Google Scholar
[12] P. Grisvard, Singularities in Boundary Value Problems, Rech. Math. Appl. 22, Masson, Paris, 1992. Search in Google Scholar
[13] L. Hurwicz, Programming in linear spaces [reprint of studies in linear and nonlinear programming, 38–102, Stanford Univ. Press, Stanford, CA, 1958], Traces and Emergence of Nonlinear Programming, Birkhäuser/Springer, Basel (2014), 131–195. 10.1007/978-3-0348-0439-4_8Search in Google Scholar
[14] K. Ito and K. Kunisch, Lagrange Multiplier Approach to Variational Problems and Applications, Adv. Des. Control 15, Society for Industrial and Applied Mathematics, Philadelphia, 2008. 10.1137/1.9780898718614Search in Google Scholar
[15] D. Kinderlehrer and G. Stampacchia, An Introduction to Variational Inequalities and Their Applications, Class. Appl. Math. 31, Society for Industrial and Applied Mathematics, Philadelphia, 2000. 10.1137/1.9780898719451Search in Google Scholar
[16] J.-L. Lions, Optimal Control of Systems Governed by Partial Differential Equations, Grundlehren Math. Wiss. 170, Springer, New York, 1971. 10.1007/978-3-642-65024-6Search in Google Scholar
[17] J.-L. Lions and G. Stampacchia, Variational inequalities, Comm. Pure Appl. Math. 20 (1967), 493–519. 10.1002/cpa.3160200302Search in Google Scholar
[18] D. G. Luenberger, Optimization by Vector Space Methods, John Wiley & Sons, New Yorky, 1969. Search in Google Scholar
[19] C. Meyer, Error estimates for the finite-element approximation of an elliptic control problem with pointwise state and control constraints, Control Cybernet. 37 (2008), no. 1, 51–83. Search in Google Scholar
[20] M. Reed and B. Simon, Methods of Modern Mathematical Physics. I. Functional Analysis, Academic Press, New York, 1972. Search in Google Scholar
[21] F. Tröltzsch, Optimal Control of Partial Differential Equations, American Mathematical Society, Providence, 2010. 10.1090/gsm/112Search in Google Scholar
[22] J. Zowe and S. Kurcyusz, Regularity and stability for the mathematical programming problem in Banach spaces, Appl. Math. Optim. 5 (1979), no. 1, 49–62. 10.1007/BF01442543Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A Nested Uzawa Solver for a Dual-Dual Mixed Finite Element Method for Frictional Contact Problems in Linear Elasticity
- An hp-Adaptive Strategy Based on Locally Predicted Error Reductions
- Analysis and Systematic Discretization of a Fokker–Planck Equation with Lorentz Force
- A Hodge Decomposition Finite Element Method for an Elliptic Maxwell Boundary Value Problem on General Polyhedral Domains
- A Galerkin Approach to the Generalized Karush–Kuhn–Tucker Conditions for the Solution of an Elliptic Distributed Optimal Control Problem with Pointwise State and Control Constraints
- Guaranteed Lower and Upper Bounds for Eigenvalues of Second Order Elliptic Operators in any Dimension
- Two Regularization Methods for Identifying the Initial Value of Time-Fractional Telegraph Equation
- Simplified Iterated Lavrentiev Regularization in Hilbert Scales
- Semi- and Fully-Discrete Analysis of Lowest-Order Nonstandard Finite Element Methods for the Biharmonic Wave Problem
- A Potential-Robust WG Finite Element Method for the Maxwell Equations on Tetrahedral Meshes
- A Conservative Eulerian Finite Element Method for Transport and Diffusion in Moving Domains
- Convergence Rates for a Finite Volume Scheme of the Stochastic Heat Equation
- A Class of Meshless Structure-Preserving Algorithms for the Nonlinear Schrödinger Equation
Articles in the same Issue
- Frontmatter
- A Nested Uzawa Solver for a Dual-Dual Mixed Finite Element Method for Frictional Contact Problems in Linear Elasticity
- An hp-Adaptive Strategy Based on Locally Predicted Error Reductions
- Analysis and Systematic Discretization of a Fokker–Planck Equation with Lorentz Force
- A Hodge Decomposition Finite Element Method for an Elliptic Maxwell Boundary Value Problem on General Polyhedral Domains
- A Galerkin Approach to the Generalized Karush–Kuhn–Tucker Conditions for the Solution of an Elliptic Distributed Optimal Control Problem with Pointwise State and Control Constraints
- Guaranteed Lower and Upper Bounds for Eigenvalues of Second Order Elliptic Operators in any Dimension
- Two Regularization Methods for Identifying the Initial Value of Time-Fractional Telegraph Equation
- Simplified Iterated Lavrentiev Regularization in Hilbert Scales
- Semi- and Fully-Discrete Analysis of Lowest-Order Nonstandard Finite Element Methods for the Biharmonic Wave Problem
- A Potential-Robust WG Finite Element Method for the Maxwell Equations on Tetrahedral Meshes
- A Conservative Eulerian Finite Element Method for Transport and Diffusion in Moving Domains
- Convergence Rates for a Finite Volume Scheme of the Stochastic Heat Equation
- A Class of Meshless Structure-Preserving Algorithms for the Nonlinear Schrödinger Equation