Home Mathematics 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
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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

  • Susanne C. Brenner EMAIL logo and Li-Yeng Sung
Published/Copyright: February 16, 2025

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.

MSC 2020: 65N30; 65K10; 49M41

1 Introduction

Let Ω d ( d = 1 , 2 , 3 ) be a bounded Lipschitz polyhedral domain, let y d L 2 ( Ω ) and let γ be a positive constant. The optimal control problem is to find

(1.1) ( y ¯ , u ¯ ) = argmin ( y , u ) 𝕂 1 2 [ y - y d L 2 ( Ω ) 2 + γ u L 2 ( Ω ) 2 ] ,

where ( y , u ) 𝕂 H 0 1 ( Ω ) × L 2 ( Ω ) if and only if

(1.2) Ω y z d x = Ω u z 𝑑 x for all  z H 0 1 ( Ω ) ,
(1.3) ϕ 1 u ϕ 2 on  Ω ,
(1.4) ψ 1 y ψ 2 on  Ω .

Here

(1.5) ϕ 1 ϕ 2  belong to  L 2 ( Ω ) ,

and

(1.6) ψ 1 , ψ 2  belong to  C ( Ω ¯ )  such that  ψ 1 < 0  and  ψ 2 > 0  on  Ω  and  ψ 1 < ψ 2  on  Ω ¯ .

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 L 2 ( Ω ) are to be understood in the almost everywhere sense.

We will write the partial differential equation (PDE) constraint (1.2) as

y = ( - Δ ) - 1 u ,

where ( - Δ ) - 1 : L 2 ( Ω ) H 0 1 ( Ω ) is the solution operator of (1.2).

From the elliptic regularity theory for polyhedral domains in [9, 12], we have

(1.7) ( - Δ ) - 1 u H 1 + α ( Ω ) C Ω u L 2 ( Ω ) for all  u L 2 ( Ω ) ,

where α ( 1 2 , 1 ] , and we also have

(1.8) H 1 + α ( Ω ) C ( Ω ¯ )

by the Sobolev embedding theorem (cf. [1]). Consequently, the constraints in (1.4) are well-defined and 𝕂 is a closed convex subset of H 0 1 ( Ω ) × L 2 ( Ω ) .

We will consider the optimal control problem (1.1)–(1.4) under the Slater condition that

(1.9) there exists  ( y * , u * ) 𝕂  such that  ψ 1 < y * < ψ 2  on  Ω ¯ .

It follows from (1.9) that the closed convex subset 𝕂 of H 0 1 ( Ω ) × L 2 ( Ω ) is nonempty and the convex optimization problem (1.1)–(1.4) has a unique solution ( y ¯ , u ¯ ) by the projection theorem in elementary Hilbert space theory (cf. [17]).

The generalized Karush–Kuhn–Tucker (KKT) conditions for ( y ¯ , u ¯ ) are given by

(1.10) Ω ( y ¯ - y d ) y 𝑑 x + γ Ω u ¯ u 𝑑 x = Ω y 𝑑 μ 1 + Ω y 𝑑 μ 2 + Ω λ 1 u 𝑑 x + Ω λ 2 u 𝑑 x

for all ( y , u ) H 0 1 ( Ω ) × L 2 ( Ω ) that satisfies (1.2), where μ 1 , μ 2 are finite Borel measures and λ 1 , λ 2 L 2 ( Ω ) such that

(1.11) μ 1 , λ 1 0 and μ 2 , λ 2 0 ,
(1.12) Ω ( y ¯ - ψ ) 𝑑 μ = 0 and Ω λ ( u ¯ - ϕ ) 𝑑 x = 0    for  = 1 , 2 .

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 ϕ 1 , ϕ 2 , ψ 1 , ψ 2 to obtain error estimates for the finite element methods (cf. [19, 4, 6]).

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 𝒯 j be a sequence of quasi-uniform triangulations (cf. [8, 5]) of Ω so that the mesh size h j of 𝒯 j decreases to 0 as j goes to , let V ~ j H 1 ( Ω ) be the P 1 finite element space associated with 𝒯 j , let V j = V ~ j H 0 1 ( Ω ) , and let W j be the space of piecewise constant functions with respect to 𝒯 j . The set of the interior vertices of 𝒯 j is denoted by 𝒱 j .

2.1 The Discrete Problem

The discrete problem for (1.1)–(1.4) is to find

(2.1) ( y ¯ j , u ¯ j ) = argmin ( y j , u j ) 𝕂 j 1 2 [ y j - y d L 2 ( Ω ) 2 + γ u j L 2 ( Ω ) 2 ] ,

where ( y j , u j ) V j × W j belongs to 𝕂 j if and only if

(2.2) Ω y j z j d x = Ω u j z j 𝑑 x for all  z j V j ,
(2.3) Q j ϕ 1 u j Q j ϕ 2 on  Ω ,
(2.4) I j ψ 1 y j I j ψ 2 on  Ω .

Here Q j is the orthogonal projection operator from L 2 ( Ω ) onto W j and I j is the nodal interpolation operator from C ( Ω ¯ ) onto V ~ j . For T 𝒯 j , the orthogonal projection from L 2 ( T ) onto constants is denoted by Q j , T .

We will write the discrete PDE constraint (2.2) as

y j = ( - Δ j ) - 1 u j ,

where ( - Δ j ) - 1 : W j V j is the solution operator of (2.2).

Remark 2.1.

The operators Q j : L 2 ( Ω ) W j and I j : C ( Ω ¯ ) V ~ j are projections that preserve signs, i.e.,

Q j ζ 0 if  ζ L 2 ( Ω )  and  ζ 0    and    I j ζ 0 if  ζ C ( Ω ¯ )  and  ζ 0 .

There are two trivial estimates

(2.5) Q j ζ L 2 ( Ω ) ζ L 2 ( Ω ) for all  ζ L 2 ( Ω ) ,
(2.6) I j ζ L ( Ω ) ζ L ( Ω ) for all  ζ C ( Ω ¯ ) .

2.2 Finite Element Estimates

We have the following standard interpolation error estimates for Q j and I j (cf. [8, 5])

(2.7) ζ - Q j ζ L 2 ( Ω ) C h j | ζ | H 1 ( Ω ) for all  ζ H 1 ( Ω ) and j 1 ,
(2.8) ζ - I j ζ H 1 ( Ω ) C h j α | ζ | H 1 + α ( Ω ) for all  ζ H 1 + α ( Ω )  and  j 1 ,
(2.9) ζ - I j ζ L ( Ω ) C h j 1 + α - d 2 | ζ | H 1 + α ( Ω ) for all  ζ H 1 + α ( Ω )  and  j 1 ,

that follow from the Bramble–Hilbert lemma (cf. [3]), scaling and (1.8).

Since α > 1 2 and smooth functions are dense in L 2 ( Ω ) and C ( Ω ¯ ) , the estimates (2.5)–(2.7) and (2.9) imply

(2.10) lim j ζ - Q j ζ L 2 ( Ω ) = 0 for all  ζ L 2 ( Ω ) ,
(2.11) lim j ζ - I j ζ L ( Ω ) = 0 for all  ζ C ( Ω ¯ ) .

Let R j : H 0 1 ( Ω ) V j be the Ritz projection operator defined by

(2.12) Ω ( R j ζ ) v j d x = Ω ζ v j d x for all  v j V j .

The following estimates for R j are also standard:

(2.13) ζ - R j ζ H 1 ( Ω ) C h j α | ζ | H 1 + α ( Ω ) for all  ζ H 1 + α ( Ω ) H 0 1 ( Ω )  and  j 1 ,

and

(2.14) ζ - R j ζ L ( Ω ) { C h j α | ζ | H 1 + α ( Ω ) for  d = 1 , C h j α ( 1 + | ln h j | ) 1 2 | ζ | H 1 + α ( Ω ) for  d = 2 , C h j α - 1 2 | ζ | H 1 + α ( Ω ) for  d = 3

for all ζ H 1 + α ( Ω ) H 0 1 ( Ω ) and j 1 .

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

(2.15) v j L ( Ω ) C h j , d v j H 1 ( Ω ) for all  v j V ~ j  and  j 1 ,

where

h j , d = { 1 for  d = 1 , ( 1 + | ln h j | ) 1 2 for  d = 2 , h j - 1 2 for  d = 3 .

Remark 2.2.

The case of (2.15) for d = 1 is the consequence of the Sobolev embedding H 1 ( Ω ) C ( Ω ¯ ) (cf. [1]). The proof of the case d = 2 can be found for example in [5, Section 4.9]. The case of d = 3 follows from the Sobolev embedding H 1 ( Ω ) L 6 ( Ω ) (cf. [1]) and the inverse estimate (cf. [8, 5])

v j L ( Ω ) C h j - 1 2 v L 6 ( Ω ) for all  v V j  and  j 1 .

2.3 Connection Between ( - Δ ) - 1 and ( - Δ j ) - 1

The following result connecting ( - Δ ) - 1 and ( - Δ j ) - 1 is crucial for the error analysis of the finite element method.

Lemma 2.3.

Let u L 2 ( Ω ) and u j W j such that lim j u - u j L 2 ( Ω ) = 0 . We have

(2.16) lim j ( - Δ ) - 1 u - ( - Δ j ) - 1 u j H 1 ( Ω ) = 0 ,
(2.17) lim j ( - Δ ) - 1 u - ( - Δ j ) - 1 u j L ( Ω ) = 0 .

In particular, the limits (2.16) and (2.17) are valid for u j = Q j u .

Proof.

Note that (1.2), (2.2) and (2.12) imply that ( - Δ j ) - 1 u j = R j ( - Δ ) - 1 u j . Since { u j } j = 1 is a bounded sequence in L 2 ( Ω ) , the estimate

(2.18) lim j ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j H 1 ( Ω ) = 0

follows immediately from (1.7) and (2.13).

Similarly, since α > 1 2 , the estimate

(2.19) lim j ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j L ( Ω ) = 0

is an immediate consequence of (1.7) and (2.14).

From (1.7) we have

( - Δ ) - 1 u - ( - Δ j ) - 1 u j H 1 ( Ω ) ( - Δ ) - 1 ( u - u j ) H 1 ( Ω ) + ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j L 2 ( Ω )
C Ω u - u j L 2 ( Ω ) + ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j L 2 ( Ω ) ,

which implies (2.16) because of (2.18).

Similarly, we have, by (1.7) and (1.8),

( - Δ ) - 1 u - ( - Δ j ) - 1 u j L ( Ω ) ( - Δ ) - 1 ( u - u j ) L ( Ω ) + ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j L ( Ω )
C Ω u - u j L 2 ( Ω ) + ( - Δ ) - 1 u j - ( - Δ j ) - 1 u j L ( Ω ) ,

which together with (2.19) implies (2.17). ∎

Since y * = ( - Δ ) - 1 u * , a simple application of Lemma 2.3 yields

(2.20) lim j ( - Δ j ) - 1 Q j u * - y * L ( Ω ) = 0 ,

and then the Slater condition (1.9) and (2.11) imply

(2.21) I j ψ 1 + β ( - Δ j ) - 1 Q j u * I j ψ 2 - β

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 j 1 .

Since Q j u * satisfies the constraints in (2.3) by (1.3) and Remark 2.1, we conclude from (2.21) that

(2.22) ( ( - Δ j ) - 1 Q j u * , Q j u * ) 𝕂 j .

Consequently, the closed convex subset 𝕂 j of V j × W j is nonempty and the convex minimization problem (2.1)–(2.4) has a unique solution ( y ¯ j , u ¯ j ) 𝕂 j characterized by the first order optimality condition (cf. [15, Chapter 1, Section 5])

(2.23) Ω ( y ¯ j - y d ) ( y j - y ¯ j ) 𝑑 x + γ Ω u ¯ j ( u j - u ¯ j ) 𝑑 x 0 for all  ( y j , u j ) 𝕂 j .

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 p j , μ j , 1 , μ j , 2 , λ j , 1 , λ j , 2 such that

(2.24) Ω ( y ¯ j - y d ) y j 𝑑 x + γ Ω u ¯ j u j 𝑑 x = Ω y j p j d x - Ω u j p j 𝑑 x + = 1 2 Ω y j 𝑑 μ j , + = 1 2 Ω λ j , u j 𝑑 x

for all ( y j , u j ) V j × W j . Here p j belongs to V j , λ j , 1 and λ j , 2 belong to W j , and μ j , 1 and μ j , 2 are sums of point measures associated with the vertices in 𝒱 j .

Moreover, we have the sign conditions

(2.25) μ j , 1 , λ j , 1 0 and μ j , 2 , λ j , 2 0 ,

and the complementarity conditions

(2.26) Ω ( y ¯ j - I j ψ ) 𝑑 μ j , = 0 and Ω λ j , ( u ¯ j - Q j ϕ ) 𝑑 x = 0 for  = 1 , 2 .

Details for the KKT conditions (2.24)–(2.26) are provided in Appendix A.

Remark 2.5.

It follows from (2.24) that

(2.27) Ω ( y ¯ j - y d ) y j 𝑑 x + γ Ω u ¯ j u j 𝑑 x = = 1 2 Ω y j 𝑑 μ j , + = 1 2 Ω λ j , u j 𝑑 x

for all ( y j , u j ) V j × W j that satisfies (2.2), which is a finite-dimensional analog of (1.10).

Remark 2.6.

The Lagrange multipliers λ j , 1 and λ j , 2 are not unique on an element T 𝒯 j where Q j , T ϕ 1 = Q j , T ϕ 2 . But their values can be assigned in a canonical way as follows.

First we note that the Lagrange multiplier p j V j is uniquely determined by the condition

(2.28) Ω y j p j d x = Ω ( y ¯ j - y d ) y j 𝑑 x - = 1 2 Ω y j 𝑑 μ j , for all  y j V j

that comes from letting u j = 0 in (2.24).

If we let y j = 0 in (2.24), then we have

(2.29) Ω ( Q j p j + γ u ¯ j ) u j 𝑑 x = Ω ( p j + γ u ¯ j ) u j 𝑑 x = Ω ( λ j , 1 + λ j , 2 ) u j 𝑑 x for all  u j W j ,

which is equivalent to

(2.30) Q j p j + γ u ¯ j = λ j , 1 + λ j , 2 .

It follows from (2.25), (2.26) and (2.29) that

Ω ( Q j p j + γ u ¯ j ) ( u j - u ¯ j ) 𝑑 x = Ω λ j , 1 ( u j - u ¯ j ) 𝑑 x + Ω λ j , 2 ( u j - u ¯ j ) 𝑑 x
(2.31) = Ω λ j , 1 ( u j - Q j ϕ 1 ) 𝑑 x + Ω λ j , 2 ( u j - Q j ϕ 2 ) 𝑑 x 0

for all u j W j that satisfy the constraints in (2.3).

We deduce from (2.6) (cf. [15, Theorem 2.3 in Chapter I]) that u ¯ j is the projection of - 1 γ Q j p j on the closed convex subset of W j defined by (2.3), with respect to the inner product of L 2 ( Ω ) . Therefore we have

(2.32) Q j , T u ¯ j = max ( Q j , T ϕ 1 , min ( Q j , T ϕ 2 , - 1 γ Q j , T p j ) ) for all  T 𝒯 j

because the characteristic functions of distinct elements of 𝒯 j are orthogonal in L 2 ( Ω ) .

It follows from (2.32) that, for any T 𝒯 j ,

(2.33) Q j , T p j + γ Q j , T u ¯ j is { > 0 only if  Q j , T u ¯ j = Q j , T ϕ 1 , < 0 only if  Q j , T u ¯ j = Q j , T ϕ 2 .

Consequently, if we define the canonical λ j , 1 and λ j , 2 by

(2.34) λ 1 , j = max ( 0 , Q j p j + γ u ¯ j ) and λ 2 , j = min ( 0 , Q j p j + γ u ¯ j ) ,

then (2.30) (and hence (2.24)) and the sign conditions for λ j , 1 and λ j , 2 in (2.25) are satisfied, and the complementarity condition for λ j , 1 and λ j , 2 in (2.26) follows from (2.33).

3 Convergence Analysis for ( y ¯ j , u ¯ j )

According to (1.7), (2.1), (2.5) and (2.22), we have

(3.1) y ¯ j - y d L 2 ( Ω ) 2 + γ u ¯ j L 2 ( Ω ) 2 ( - Δ j ) - 1 Q j u * - y d L 2 ( Ω ) 2 + γ Q j u * L 2 ( Ω ) 2 C

for j 1 . This simple estimate is a cornerstone for the convergence analysis.

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 𝕂 j .

Lemma 3.1.

There exists a sequence of positive numbers ϵ j such that ϵ j 0 as j and

( ( - Δ j ) - 1 [ ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ] , ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ) 𝕂 j for  j 1 .

Proof.

It suffices to find ϵ j > 0 such that lim j ϵ j = 0 and ( - Δ j ) - 1 [ ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ] satisfies the constraints in (2.4), because ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * satisfies (2.3) for any ϵ j ( 0 , 1 ) .

Since y ¯ = ( - Δ ) - 1 u ¯ , it follows from (2.17) that

(3.2) lim j ( - Δ j ) - 1 Q j u ¯ - y ¯ L ( Ω ) = 0 .

In view of the constraints in (1.4) for y ¯ , Remark 2.1 and (2.6), we have

( - Δ j ) - 1 Q j u ¯ - I j ψ 1 = I j ( - Δ j ) - 1 Q j u ¯ - I j y ¯ + I j ( y ¯ - ψ 1 ) - ( - Δ j ) - 1 Q j u ¯ - y ¯ L ( Ω ) ,

and hence

(3.3) I j ψ 1 - δ j ( - Δ j ) - 1 Q j u ¯ on  Ω ¯ ,

where δ j = ( - Δ j ) - 1 Q j u ¯ - y ¯ L ( Ω ) . Similarly we have

(3.4) ( - Δ j ) - 1 Q j u ¯ I j ψ 2 + δ j on  Ω ¯ .

Let β be the constant in (2.21) and ϵ j = δ j β . Then lim j ϵ j = 0 by (3.2) and according to (2.21), (3.3) and (3.4), for j sufficiently large so that 0 < ϵ j < 1 ,

( - Δ j ) - 1 [ ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ] ( 1 - ϵ j ) ( I j ψ 1 - δ j ) + ϵ j ( I j ψ 1 + β ) I j ψ 1 + ϵ j δ j ,
( - Δ j ) - 1 [ ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ] ( 1 - ϵ j ) ( I j ψ 2 + δ j ) + ϵ j ( I j ψ 2 - β ) I j ψ 2 - ϵ j δ j .

3.2 Reduced Optimization Problems

Let J ( ) be defined by

(3.5) J ( u ) = 1 2 [ ( - Δ ) - 1 u - y d L 2 ( Ω ) 2 + γ u L 2 ( Ω ) 2 ] for all  u L 2 ( Ω ) .

Then J ( ) is a smooth strictly convex function and we can rewrite (1.1)–(1.4) as the following equivalent problem:

(3.6) Find u ¯ = argmin u U ad J ( u ) ,

where the closed convex subset U ad of L 2 ( Ω ) is defined by

(3.7) U ad = { u L 2 ( Ω ) : ( ( - Δ ) - 1 u , u ) 𝕂 } .

Similarly, let J j ( ) be defined by

(3.8) J j ( u j ) = 1 2 [ ( - Δ j ) - 1 u j - y d L 2 ( Ω ) 2 + γ u j L 2 ( Ω ) 2 ] for all  u j W j .

Then J j ( ) is a smooth strictly convex function and we can rewrite (2.1)–(2.4) as the following equivalent problem:

(3.9) Find u ¯ j = argmin u j U ad , j J j ( u ) ,

where the closed convex subset U ad , j of W j is defined by

(3.10) U ad , j = { u j W j : ( ( - Δ j ) - 1 u j , u j ) 𝕂 j } .

In particular, the sequence from Lemma 3.1 satisfies

(3.11) ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * U ad , j for  j 1 .

Remark 3.2.

Let u j U ad , j and y j = ( - Δ j ) - 1 u j . It follows from (3.8) that

J j ( u j ) - J j ( u ¯ j ) = 1 2 [ y j - y ¯ j L 2 ( Ω ) 2 + γ u j - u ¯ j L 2 ( Ω ) 2 ] + Ω ( y ¯ j - y d ) ( y j - y ¯ j ) 𝑑 x + γ Ω u ¯ j ( u j - u ¯ j ) 𝑑 x ,

where y ¯ j = ( - Δ j ) - 1 u ¯ j . In view of (2.23) and (3.10), we have

(3.12) γ 2 u j - u ¯ j L 2 ( Ω ) 2 J j ( u j ) - J j ( u ¯ j ) for all  u j U ad , j .

The connection between J ( ) and J j ( ) is provided in the next lemma.

Lemma 3.3.

Let u j W j for j 1 . If { u j } j = 1 is a bounded sequence in L 2 ( Ω ) , then

(3.13) lim j [ J ( u j ) - J j ( u j ) ] = 0 .

If u L 2 ( Ω ) and u - u j L 2 ( Ω ) 0 as j , then

(3.14) lim j J j ( u j ) = J ( u ) .

Proof.

Let u j W j and v L 2 ( Ω ) be arbitrary. It follows from (3.5), (3.8) and the triangle inequality that

| J ( v ) - J j ( u j ) | 1 2 ( - Δ ) - 1 v - ( - Δ j ) - 1 u j L 2 ( Ω ) ( 2 ( - Δ ) - 1 v - y d L 2 ( Ω ) + ( - Δ j ) - 1 u j - ( - Δ ) - 1 v L 2 ( Ω ) )
+ γ 2 v - u j L 2 ( Ω ) ( v L 2 ( Ω ) + u j L 2 ( Ω ) ) .

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 u j to u in L 2 ( Ω ) by using the basic techniques of the direct method in the calculus of variations.

Theorem 3.4.

We have

(3.15) lim j u ¯ - u ¯ j L 2 ( Ω ) = 0 .

Proof.

It suffices to show that any subsequence of u j (still denoted by u j for simplicity of notation) has a subsequence that satisfies the limit (3.15).

Let ϵ j be the number that appears in Lemma 3.1. It follows from (2.10), (3.9), (3.11) and (3.14) that

(3.16) J j ( u ¯ j ) J j ( ( 1 - ϵ j ) Q j u ¯ + ϵ j Q j u * ) J ( u ¯ ) as  j .

Let I = inf j J ( u ¯ j ) . In view of (3.1) and the weak compactness of bounded subsets of L 2 ( Ω ) (cf. [11, Section 1.9]), there exists a subsequence { u ¯ j n } n = 1 such that

(3.17) lim n J ( u ¯ j n ) = I

and u ¯ j n converges weakly in L 2 ( Ω ) to a function u . By the weak lower semi-continuity of the continuous convex function J ( ) (cf. [10, Corollary 2.2]), we have

(3.18) J ( u ) I .

Let χ B be the characteristic function of an arbitrary Borel set B Ω . The constraints in (2.3), (2.10) and the weak convergence of u ¯ j n to u in L 2 ( Ω ) imply

Ω ( u ¯ - ϕ ) χ B 𝑑 x = lim n Ω ( u ¯ j n - Q j n ϕ ) χ B 𝑑 x = { a nonnegative number if  = 1 , a nonpositive number if  = 2 .

Consequently, we have ϕ 1 u ϕ 2 .

The weak convergence of u ¯ j n to u also implies

(3.19) ( - Δ ) - 1 ( u ¯ j n - u ) L ( Ω ) = 0

because H 1 + α ( Ω ) is compactly embedded in C ( Ω ¯ ) (cf. [1, Theorem 6.3]) and therefore the weak convergence of u ¯ j n in L 2 ( Ω ) is converted into the strong convergence of ( - Δ ) - 1 u ¯ j n in C ( Ω ¯ ) (cf. [20, Theorem VI.11]). It follows from (2.19), (3.1) and (3.19) that y = ( - Δ ) - 1 u satisfies

lim n ( - Δ j n ) - 1 u ¯ j n - y L ( Ω ) = 0 ,

and hence ψ 1 y ψ 2 on Ω ¯ by (2.4) and (2.11).

Therefore u belongs to U ad defined by (3.7) and consequently (3.6) implies

(3.20) J ( u ¯ ) J ( u ) .

Putting (2.18), (3.1), (3.13), (3.16)–(3.18) and (3.20) together, we see that

J ( u ¯ ) J ( u ) I = lim n J ( u ¯ j n ) = lim n J j n ( u ¯ j n ) J ( u ¯ ) ,

which together with (3.16) implies

(3.21) lim n [ J j n ( ( 1 - ϵ j ) Q j n u ¯ + ϵ j n Q j n u * ) - J j n ( u ¯ j n ) ] = J ( u ¯ ) - J ( u ¯ ) = 0 .

Finally, we find, from (3.12) and (3.21),

lim n ( ( 1 - ϵ j n ) Q j n u ¯ + ϵ j n Q j n u * ) - u ¯ j n L 2 ( Ω ) = 0 ,

and then (2.10) implies

lim n u ¯ - u ¯ j n L 2 ( Ω ) = 0 .

Combining Lemma 2.3 and Theorem 3.4, we arrive at the following corollary.

Corollary 3.5.

We have

(3.22) lim j y ¯ - y ¯ j H 1 ( Ω ) = 0 ,
(3.23) lim j y ¯ - y ¯ j L ( Ω ) = 0 .

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 μ j , 1 and μ j , 2 , where the discrete Slater condition (2.21) plays a crucial role.

Lemma 4.1.

There exists a positive constant C such that

(4.1) μ j , 1 ( Ω ) - μ j , 2 ( Ω ) C for  j 1 .

Proof.

Substituting y j = ( - Δ j ) - 1 Q j u * - y ¯ j and u j = Q j u * - u ¯ j in (2.27), we find

= 1 2 Ω ( ( - Δ j ) - 1 Q j u * - y ¯ j ) 𝑑 μ j , + = 1 2 Ω λ j , ( Q j u * - u ¯ j ) 𝑑 x
(4.2) = Ω ( y ¯ j - y d ) ( ( - Δ j ) - 1 Q j u * - y ¯ j ) 𝑑 x + γ Ω u ¯ j ( Q j u * - u ¯ j ) 𝑑 x .

Note that

(4.3) = 1 2 Ω λ j , ( Q j u * - u ¯ j ) 𝑑 x = = 1 2 Ω λ j , ( Q j u * - Q j ϕ ) 𝑑 x 0

by (2.3), (2.25) and (2.26), and

= 1 2 Ω ( ( - Δ j ) - 1 Q j u * - y ¯ j ) 𝑑 μ j , = = 1 2 Ω ( ( - Δ j ) - 1 Q j u * - I j ψ ) 𝑑 μ j ,
(4.4) β [ μ j , 1 ( Ω ) - μ j , 2 ( Ω ) ]

by (2.21), (2.25) and (2.26).

The estimate (4.1) follows from (3.1) and (4.1)–(4.1). ∎

4.2 A Bound for p j

Next we find a bound for the function p j V j that appears in (2.24).

Lemma 4.2.

There exists a constant C such that

(4.5) p j L 2 ( Ω ) C for  j 1 .

Proof.

Let p ~ j L 2 ( Ω ) be defined by

(4.6) Ω p ~ j u 𝑑 x = Ω ( y ¯ j - y d ) ( - Δ ) - 1 u 𝑑 x - = 1 2 Ω ( - Δ ) - 1 u 𝑑 μ j , for all  u L 2 ( Ω ) .

It follows from (1.7), (1.8), (3.1) and (4.1) that the right-hand side of (4.6) is bounded by C u L 2 ( Ω ) , where the constant C is independent of j. Therefore we have

(4.7) p ~ j L 2 ( Ω ) C for  j 1 .

Let u L 2 ( Ω ) be arbitrary. We have, by (1.7), (2.12)–(2.14), (2.28), (3.1), (4.1) and (4.6),

Ω ( p ~ j - p j ) u 𝑑 x = Ω ( y ¯ j - y d ) ( - Δ ) - 1 u 𝑑 x - = 1 2 Ω ( - Δ ) - 1 u 𝑑 μ j , - Ω ( ( - Δ ) - 1 u ) p j d x
= Ω ( y ¯ j - y d ) ( - Δ ) - 1 u 𝑑 x - = 1 2 Ω ( - Δ ) - 1 u 𝑑 μ j , - Ω R j ( ( - Δ ) - 1 u ) p j d x
= Ω ( y ¯ j - y d ) [ ( - Δ ) - 1 u - R j ( ( - Δ ) - 1 u ) ] 𝑑 x - = 1 2 Ω [ ( - Δ ) - 1 u - R j ( ( - Δ ) - 1 u ) ] 𝑑 μ j ,
C u L 2 ( Ω ) ,

which implies

(4.8) p ~ j - p j L 2 ( Ω ) C for  j 1 .

The estimate (4.5) follows from (4.7) and (4.8). ∎

4.3 Bounds for λ j , 1 and λ j , 2

Now we establish the bounds for λ j , 1 and λ j , 2 .

Lemma 4.3.

There exists a positive constant C such that

(4.9) λ j , L 2 ( Ω ) C for  = 1 , 2 and  j 1 .

Proof.

It follows from Remark 2.6 that we can assume λ j , 1 and λ j , 2 are given by (2.34). Therefore the estimate (4.9) follows from (2.5), (3.1) and (4.5). ∎

4.4 Generalized KKT Conditions

By Lemma 4.1, Lemma 4.3 and the weak compactness of bounded Borel measures and bounded functions in L 2 ( Ω ) (cf. [11, Section 1.9]), there exist Borel measures μ 1 0 , μ 2 0 and functions λ 1 0 , λ 2 0 in L 2 ( Ω ) such that the subsequence μ j n , (resp. λ j n ) converges weakly to μ (resp., λ ) for = 1 , 2 .

In view of (2.10), (2.11), (2.26), (3.15), (3.23), the complementarity conditions in (1.12) follow from the limits

Ω ( y ¯ - ψ ) 𝑑 μ = lim j Ω ( y ¯ j - I j ψ ) 𝑑 μ j , = 0 for  = 1 , 2 ,
Ω λ ( u ¯ - ϕ ) 𝑑 x = lim j Ω λ j , ( u ¯ j - Q j ϕ ) 𝑑 x = 0 for  = 1 , 2 .

Let ( y , u ) H 0 1 ( Ω ) × L 2 ( Ω ) satisfy (1.2) and

( y j n , u j n ) = ( ( - Δ j ) - 1 Q j n u , Q j n u ) .

Then we have, by (2.27),

(4.10) Ω ( y ¯ j n - y d ) y j n 𝑑 x + γ Ω u ¯ j n u j n 𝑑 x = = 1 2 Ω y j n 𝑑 μ j n , + = 1 2 Ω λ j n , u j n 𝑑 x for  n 1 .

It follows from (2.10) that

lim n u - u j n L 2 ( Ω ) = 0 ,

and then Lemma 2.3 yields

lim n y - y j n L 2 ( Ω ) = 0 and lim n y - y j n L ( Ω ) = 0 .

Therefore, in view of (3.22) and (3.23), we obtain (1.10) by letting n in (4.10).

Remark 4.4.

Let the space E ̊ ( Δ ; L 2 ( Ω ) ) be defined by

E ̊ ( Δ ; L 2 ( Ω ) ) = { v H 0 1 ( Ω ) : Δ v L 2 ( Ω ) } ,

where Δ v is understood in the sense of distributions.

We can rewrite (1.10) as the following analog of (2.24):

Ω ( y ¯ - y d ) y 𝑑 x + γ Ω u ¯ u 𝑑 x = Ω ( - Δ y - u ) p 𝑑 x + = 1 2 Ω y 𝑑 μ + = 1 2 Ω λ u 𝑑 x

for all ( y , u ) E ̊ ( Δ ; L 2 ( Ω ) ) × L 2 ( Ω ) , where p L 2 ( Ω ) .

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 ( y ¯ , u ¯ ) (cf. for example [19, 4, 6]). The convergence analysis in this paper only requires the minimal assumptions (1.5) and (1.6) on the constraint functions that ensure the well-posedness of the optimal control problem and hence it fills a gap in the literature.

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]).

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 m j (resp. n j ) be the dimension of V j (resp., W j ). We can represent a function y j V j by the vector 𝒚 j m j such that 𝒚 j ( k ) = y j ( p k ) , where p 1 , , p m j 𝒱 j are the interior vertices of 𝒯 j , and we can represent a function u j W j by the vector 𝒖 j n j such that 𝒖 j ( k ) = u j | T k , where T 1 , , T n j are the elements of 𝒯 j . Below the inequalities between vectors are to be understood in the componentwise sense.

The minimization problem (2.1)–(2.4) can be rewritten as

(A.1) minimize 1 2 [ ( 𝒚 - 𝒚 d , j ) t 𝑴 j ( 𝒚 - 𝒚 d , j ) + γ 𝒖 t 𝑵 j 𝒖 ]

subject to the constraints

(A.2) 𝑨 j 𝒚 j = 𝑩 j 𝒖 j , ϕ 1 , j 𝒖 j ϕ 2 , j and 𝝍 1 , j 𝒚 j 𝝍 2 , j ,

where ϕ j , n j ( = 1 , 2 ) is the vector representing Q j ϕ , 𝝍 j , m j ( = 1 , 2 ) is the vector representing I j ψ , the matrices 𝑨 j m j × m j , 𝑴 j m j × m j , 𝑵 j n j × n j and 𝑩 j m j × n j are defined by

(A.3) 𝒚 j t 𝑨 j 𝒛 j = Ω y j z j d x for all  𝒚 j , 𝒛 j m j ,
(A.4) 𝒚 j t 𝑴 j 𝒛 j = Ω y j z j 𝑑 x for all  𝒚 j , 𝒛 j m j ,
(A.5) 𝒖 j t 𝑵 j 𝒗 j = Ω u j v j 𝑑 x for all  𝒖 j , 𝒗 j n j ,
(A.6) 𝒛 j t 𝑩 𝒖 j = Ω u j z j 𝑑 x for all  𝒖 j n j , 𝒛 j m j ,

and 𝒚 d , j m j is defined by

(A.7) 𝒚 d , j t 𝑴 j 𝒚 j = Ω y d y j 𝑑 x for all  𝒚 j m j .

Let ( 𝒚 ¯ j , 𝒖 ¯ j ) m j × n j be the solution of (A.1)–(A.2). Since the constraints in (A.2) are affine constraints, we can apply a well-known constraint qualification (cf. [2, CQ3 in Section 5.4]) to conclude that there exist Lagrange multipliers 𝒑 j , 𝝁 j , 1 , 𝝁 j , 2 m j and 𝝀 j , 1 , 𝝀 j , 2 n j such that

(A.8) ( 𝒚 ¯ j - 𝒚 d , j ) t 𝑴 j 𝒚 j + γ 𝒖 ¯ j t 𝑵 j 𝒖 j = 𝒑 j t ( 𝑨 j 𝒚 j - 𝑩 j 𝒖 j ) + = 1 2 𝝁 j , t 𝒚 j + = 1 2 𝝀 j , t 𝒖 j

for all ( 𝒚 j , 𝒖 j ) m j × n j ,

(A.9) 𝝁 j , 2 𝟎 m j × 1 𝝁 j , 1 and 𝝀 j , 2 𝟎 n j × 1 𝝀 j , 1 ,

and for = 1 , 2 ,

(A.10) 𝝁 j , t ( 𝒚 ¯ j - 𝝍 j , ) = 0 and 𝝀 j , t ( 𝒖 ¯ j - ϕ j , ) = 0 .

Using (A.3)–(A.7), we can translate (A.8)–(A.10) into (2.24)–(2.26) by taking μ j , = p k 𝒱 j 𝝁 j , ( k ) δ p k , where δ p k is the Dirac point measure associated with the vertex p k , and by taking λ j , = 𝝀 j , ( k ) / | T k | on the element T k ( 1 k n j ) of 𝒯 j .

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Received: 2025-01-11
Accepted: 2025-02-10
Published Online: 2025-02-16
Published in Print: 2025-10-01

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