Optimal Control of Abstract Elliptic Variational Inequalities with State Constraints

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Optimal Control of Abstract Elliptic Variational Inequalities with State Constraints

Maïtine Bergounioux

To cite this version:

Maïtine Bergounioux. Optimal Control of Abstract Elliptic Variational Inequalities with State Con-

straints. SIAM Journal on Control and Optimization, Society for Industrial and Applied Mathematics,

1998, 36, n°1, pp.273-289. �hal-00022987�

OPTIMAL CONTROL OF PROBLEMS GOVERNED BY ABSTRACT ELLIPTIC VARIATIONAL INEQUALITIES WITH

STATE CONSTRAINTS

MA¨ITINE BERGOUNIOUX URA-CNRS 1803, Universit´ e d’Orl´ eans,

U.F.R. Sciences, B.P. 6759, F-45067 Orl´ eans Cedex 2, France

E-mail: [email protected]

Abstract. In this paper we investigate optimal control problems governed by elliptic variational inequalities with additional state-constraints. We present a relaxed formulation for the problem. With penalization methods and approximation techniques we give qualifi- cation conditions to get first order optimality conditions.

Key Words : State and Control Constrained Optimal Control Problems, Variational Inequalities, Optimality Conditions.

AMS Subject Qualification : 49J20, 49M29

1. Introduction. In this paper we investigate optimal control problems governed by elliptic variational inequalities with additional state-constraints.

This topic has been widely studied by many authors. We mainly could mention Barbu [1, 2, 3], Friedman [11, 12], Mignot [13], Mignot & Puel [14], Tiba [15]

or Bermudez & Saguez [8]. Most of these contributions (for example [1, 2, 3]) study the problem via the penalization of the state (in)equation. On the other hand Mignot & Puel [14] (for instance) give an equivalent formulation of the variational inequality via the associated lagrange multiplier for the obstacle problem example. We have followed this point of view; our purpose is to set optimality conditions for such a problem that could easily be used from the numerical point of view. This paper is the generalization of the case of the obstacle problem that we have been studying in [5]. We deal here with quite abstract variational inequalities. Following our previous work, we first present a relaxed form of the original problem which can be considered as a good “ap- proximation” of this problem. Then using both Moreau-Yosida approximation techniques and a penalization method we are able to set optimality conditions.

We end the paper with the example of the obstacle problem.

2. Setting the Problem. Let V and H be a pair of real Hilbert spaces such that V is a dense subset of H and V ⊂ H ⊂ V ⁰ algebraically and topologically (V ⁰ denotes the dual of V ). We suppose in addition that

the injection V ⊂ H is compact

(2.1)

so that H ⊂ V ⁰ is compact too. (For example one may choose V = H _o ¹ (Ω) and H = L ² (Ω) where Ω is an open bounded “regular” subset of IR ³ .) We denote h , i the pairing between V and V ⁰ , ( , ) _H the H-scalar product and

| | _V the norm of V . We call Λ _V : V → V ⁰ the canonical isomorphism. Let U be another Hilbert-space (such that U = U ⁰ );we consider the variational inequality

Ay + ∂Φ(y) 3 Bu + f , (2.2)

where

• A : V → V ⁰ is a linear, continuous operator satisfying the coercivity condition

∃ω > 0, ∀v ∈ V hAv, vi ≥ ω|v| ² _V ; (2.3)

•

Φ is a convex, proper, lower semi-continuous (lsc) function from V to IR ∪ {+∞} ;

(2.4) We denote

dom Φ = { y ∈ V | Φ(y) < +∞ }

the domain of Φ (which is convex and V -closed). We recall that the subdiffer- ential ∂Φ(y _o ) of Φ at y _o ∈ V is :

∂Φ(y o ) = { z ^∗ ∈ V ⁰ | ∀y ∈ V Φ(y) − Φ(y o ) − hz ^∗ , y − y o i ≥ 0} , and that dom Φ = dom ∂Φ.

• f ∈ V ⁰ and

B is a linear, compact operator from U to V ⁰ . (2.5)

Let us recall some general results about solutions of (2.2)(see [2, 3] for exam- ple).

Theorem 2.1. (Barbu [2] p 40) Under assumptions (2.3)-(2.4) and for all ψ ∈ V ⁰ the variational inequality

Ay + ∂Φ(y) 3 ψ

has a unique solution y(ψ) ∈ V and the mapping ψ 7→ y(ψ) is Lipschitz from V ⁰ to V .

Corollary 2.1. (Barbu [2] p 63) With the assumptions of the previous theorem, for all u ∈ U , there exists a unique y(u) ∈ V solution of (2.2) and the mapping u 7→ y(u) is weakly-strongly continuous from U to V .

In order to get some regularity results, we suppose from now that f ∈ H and B ∈ L(U, H ) ,

(2.6)

so that (2.5) is fulfilled and we may use in addition the following result (Barbu [2] p 42) :let us denote A H : H → H the operator

A _H (y) = Ay for all y ∈ D(A _H ) = { y ∈ V | Ay ∈ H } . (2.7)

This operator is maximal monotone in H × H and we have

Theorem 2.2. Assume (2.3) and suppose in addition that there exists z ∈ H and c ∈ IR such that

∀y ∈ V, ∀λ > 0 Φ((I + λA _H ) ⁻¹ (y + λz)) ≤ Φ(y) + cλ . (2.8)

Then for every ψ ∈ H the solution y(ψ) of Ay + ∂Φ(y) 3 ψ belongs to D(A _H ) and

|Ay(ψ)| _H ≤ c(1 + |ψ| _H ) .

From now we suppose that (2.8) is ensured. This is the case for example for the obstacle problem given as an example in the last section of this paper, where V = H _o ¹ (Ω), H = L ² (Ω) and D(A _H ) = H ² (Ω) ∩ H _o ¹ (Ω).

Applying this regularity result to our case we get that for all u ∈ U , f + Bu ∈ H so that the solution of (2.2) y belongs to D(A _H ) ⊂ V , that is Ay ∈ H.

Remark 2.1. One could think that this regularity assumption is not re- ally necessary. Indeed, it is not useful to prove the results of next section.

Nevertheless, when we investigate penalized problems, then we shall need some

“strong” convergence for the penalized solutions, that is with the compactness assumptions “weak” convergence in the pivot-space H.

Now, we investigate the following optimal control problem

(P )



 

 

min g(y) + h(u) Ay + ∂Φ(y) 3 Bu + f , (y, u) ∈ K × U _ad , where

• g is convex from H to IR, finite everywhere ( dom(g) = H) and continuous.

This implies ([4] proposition 1.9 p.85) that

∃(a _g , c g ) ∈ H × IR such that ∀y ∈ H g(y) ≥ (a g , y) _H + c g

(2.9)

(because g is lsc) and g is everywhere subdifferentiable.

• h is convex from U to IR, finite everywhere ( dom(h) = U ), continuous and coercive :

|u|

lim →+∞

h(u)

|u| _U = +∞ ;

(2.10)

• U _ad (resp. K) is a closed, convex, non empty subset of U (resp. V ). We note that y ∈dom ∂Φ ⊂dom Φ so that one may always suppose that

K ⊂ dom Φ . (2.11)

Remark 2.2. From now, we always suppose that these assumptions are satisfied. They are not of course optimal. To get more information one can refer to Barbu [3] p. 150.

We end this section with an existence result for (P ).

Theorem 2.3. Under assumptions (2.3),(2.4),(2.9),(2.10), problem (P ) has (at least) one optimal solution.

Proof - The proof is quite similar to the one given in Barbu [3] p 151. The main difference is the addition of the state constraint y ∈ K which does not modify the proof.

3. “Relaxation” of the Problem. We denote Φ ^∗ : V ⁰ → IR the conju- gate function of Φ; it is also a convex, proper, lsc and and we know that (see [4, 10])

z ∈ ∂Φ(y) ⇔ y ∈ ∂Φ ^∗ (z) ⇔ Φ(y) + Φ ^∗ (z) = hy, zi . (3.1)

Because of the regularity result we always have Bu + f − Ay ∈ H , so that z = Bu + f − Ay ∈ ∂Φ(y) ∩ H and (3.1) is equivalent to

z ∈ ∂Φ(y) ⇔ y ∈ ∂Φ ^∗ (z) ⇔ Φ(y) + Φ ^∗ (z) = (y, z) _H .

Remark 3.1. In addition such an element z belongs to dom ∂Φ ^∗ ⊂ dom Φ ^∗ so that the condition “z ∈ dom Φ ^∗ ” is implicitly included in relation (3.1).

Finally, problem (P ) is equivalent to

( P ^e )



 

 

 

 

min g(y) + h(u) Ay = Bu + f − z ∈ H , Φ(y) + Φ ^∗ (z) − (y, z) _H = 0 ,

y ∈ D(A H ) ∩ K, (u, z) ∈ U ad × (dom Φ ^∗ ∩ H) .

w = (u, z) is now considered as a new control variable. Problem ( P) is a state- ^e constrained optimal control problem with a non-convex (because of the bilinear term) constraint coupling the state y and the control w. This constraint is quite difficult to deal with. It is not convex and the equality constraint makes the interior of the feasible domain empty in a very strong sense. So as we have done in [5] for the particular case of the obstacle problem we had rather study a “relaxed” problem. More precisely we consider

(P _α ^R )



 

 

 

 

min g(y) + h(u) Ay = Bu + f − z ,

Φ(y) + Φ ^∗ (z) − (y, z) _H ≤ α ,

y ∈ K, (u, z) ∈ U _ad × B _R ^∗ ,

where α > 0, R > 0, B _R ^∗ = B _H (0, R)∩dom Φ ^∗ and B _H (0, R) is the H-ball of radius R. B _R ^∗ is convex and H-closed (since dom Φ ^∗ is convex and V ⁰ -closed ).

Remark 3.2. Let us comment this “relaxed” form for problem ( P ^e ). First we know that Φ(y) + Φ ^∗ (z) − (y, z) _H is always nonnegative. So

Φ(y) + Φ ^∗ (z) − (y, z) _H ≤ α (3.2)

is equivalent to |Φ(y) + Φ ^∗ (z) − (y, z) _H | ≤ α. This is the relaxed term : we have replaced the equality “ = 0” with the inequality “ ≤ α ”, where α may be as small as wanted. This is quite realistic from the numerical point of view where equalities are indeed inequalities up to α.

On the other hand, if we do not add the constraint “z ∈ B _H (0, R)” the relaxed problem is not coercive and so in general it has no solution. Moreover, by virtue of assumptions (2.8) and (2.10) the optimal solutions (y, u) and Ay remain in a bounded set of H × U and H and the constant R has to be chosen accordingly (that is large enough); in particular R is greater than |A¯ y−f −B u| ¯ _H for any (¯ y, u) ¯ solution of (P), so that the feasible domain of (P _α ^R ) is non empty.

Anyway, this additional condition is not very restrictive. One could instead add a regularization term (as |z| ² _H /R) to the cost functional, which would have exactly the same effect. One may also add adapted penalization terms to this cost functional as |y − y| ¯ ² _V or |z − z| ¯ ² _H .

From now we fix R so that we always omit the index R in the notations and (P _α ^R ) becomes (P _α ).

Theorem 3.1. For every α > 0, (P _α ) has at least one optimal solution denoted (y α , u α , z α ). Moreover, when α → 0, y α strongly converges to y ¯ in V , u _α weakly converges to u ¯ in U and z _α weakly converges to z ¯ in H where (¯ y, u) ¯ is a solution of (P ) and z ¯ = A y ¯ − B u ¯ − f ∈ H.

Proof - Let α > 0; we have chosen R such that the feasible domain of (P _α ) is always non-empty. We first prove that that d α = inf (P _α ) ∈ IR.The coercivity and continuity assumptions on A yield that A is an isomorphism from V to V ⁰ . Let (y n , u n , z n ) be a minimizing sequence : y n = A ⁻¹ (Bu n + f − z n ), |z _n | _H ≤ R, u n ∈ U _ad , Φ(y n ) + Φ ^∗ (z n ) − (y n , z n ) _H ≤ α and g(y n ) +h(u n ) → d α . Because of (2.9) we have

g(y _n ) + h(u _n ) ≥ a _g , A ⁻¹ (Bu _n + f − z _n ) _H + h(u _n ) + c _g

≥ a _g , A ⁻¹ Bu _{n H} + h(u _n ) − a _g , A ⁻¹ z _{n H} + c _g + a _g , A ⁻¹ f _H . As |z _n | _H ≤ R then − a _g , A ⁻¹ z _{n H} + c _g + a _g , A ⁻¹ f _H is bounded from below.

If d α = −∞ then a g , A ⁻¹ Bu n

H + h(u n ) → −∞. If (u _n ) were bounded in U then (extracting a subsequence) u n would be weakly convergent to some ˜ u in U ; as B is continuous Bu _n would be convergent to B u ˜ weakly in H and strongly in V ⁰ . Therefore A ⁻¹ Bu n would be strongly convergent to A ⁻¹ B u ˜ in V and a g , A ⁻¹ Bu n

H → a g , A ⁻¹ B u ˜ _H . Moreover h is lsc, so that −∞ <

h(˜ u)≤ lim inf

n→+∞ h(u _n ) ; so we get a contradiction. This means that (u _n ) is unbounded. Coercivity assumption (2.10) implies that

n→+∞ lim h(u _n )

|u _n | _U = +∞ . Moreover Cauchy-Schwartz inequality shows that

a _g , A ⁻¹ Bu _{n H}

|u _n | _U

≤ c _o |u _n | _U

|u _n | _U ; So

n→+∞ lim

a g , A ⁻¹ Bu n

H + h(u n ) = |u _n | _U

"

a _g , A ⁻¹ Bu _{n H}

|u _n | _U + h(u n )

|u _n | _U

#

= +∞ , and we get a contradiction.

• As |z _n | _H ≤ R one may extract a subsequence (still denoted z _n ) weakly con- vergent in H to z α ∈ B ^∗ _R (since B _R ^∗ is weakly closed). As d α > −∞, h(u _n ) is bounded , and by coercivity (u n ) is bounded in U ; so (extracting a sub- sequence) u _n weakly converges to u _α ∈ U _ad (U _ad is weakly closed in U ).

The continuity of B yields that Bu n converges to Bu α weakly in H. So Ay n = Bu n + f −z _n converges to Bu α +f − z α weakly in H and strongly in V ⁰ . As A is an isomorphism from V to V ⁰ , y _n converges to y _α = A ⁻¹ (Bu _α +f − z _α ) strongly in V . Moreover y _α ∈ K since K is closed.

• Let us prove that (y α , u α , z α ) is feasible for (P _α ). It remains to show that Φ(y _α ) + Φ ^∗ (z _α ) − (y _α , z _α ) _H ≤ α. Φ and Φ ^∗ are convex and lower semi- continuous so they are weakly lower semi-continuous and we have

Φ(y α ) + Φ ^∗ (z α ) ≤ lim inf

n→+∞ Φ(y n ) + lim inf

n→+∞ Φ ^∗ (z n ) ≤ lim inf

n→+∞ [Φ(y n ) + Φ ^∗ (z n )] . Moreover the strong convergence of y n to y α in H and the weak convergence of z _n to z _α in H gives

n→+∞ lim (y _n , z _n ) _H = (y _α , z _α ) _H . Finally

Φ(y _α ) + Φ ^∗ (z _α ) − (y _α , z _α ) _H ≤ lim inf

n→+∞ [Φ(y _n ) + Φ ^∗ (z _n ) − (y _n , z _n ) _H ] ≤ α . Therefore (y _α , u _α , z _α ) is feasible for (P _α ) and g(y _α ) + h(u _α ) ≥ d _α . As g and h are lsc we have also

g(y α ) + h(u α ) ≤ lim inf

n→+∞ g(y n ) + lim inf

n→+∞ h(u n ) ≤ lim inf

n→+∞ [g(y n ) + h(u n )] = d α .

Finally g(y α ) + h(u α ) = d α and (y α , u α , z α ) is an optimal solution for (P _α ).

• It remains to prove the convergence of (y _α , u _α , z _α ) to an optimal solution of (P). Let (y o , u o , z o ) be an optimal solution of (P ) such that z o ∈ B _R ^∗ (remember that we have chosen R to ensure the existence of such a solution).

It is also a feasible triple for (P _α ), for any α > 0. So

∀α > 0 − ∞ < d _α = g(y _α ) + h(u _α ) ≤ g(y _o ) + h(u _o ) = d _o .

So d _α is bounded from above in IR. If it were not bounded from below then we could find a sequence α _n → 0 such that d _α

→ −∞. The same proof as before shows that it is impossible. So h(u α ) is bounded (independently of α) and by coercivity (u _α ) is bounded in U as well. Similarly (z _α ) is bounded in H (z _α ∈ B _R ^∗ ). Then one can show (as we have proved the existence of (y α , u α , z α )) that

y α → y ¯ strongly in V , u α * u ¯ weakly in U and z α * z ¯ weakly in H , where (¯ y, u) is a solution of (P) with ¯ ¯ z = A¯ y − B u ¯ − f and that

α→0 lim [g(y _α ) + h(u _α )] = g(¯ y) + h(¯ u) .

4. Penalization of (P _α ).

4.1. The approximated-penalized problem. From now, we fix also α > 0 as small as wanted and we shall omit the index α most of time. We are going to approximate and penalize the state-equation of (P _α ) to get an approximated problem (P _α ^ε ). Then we shall derive optimality conditions for this problem and set qualification conditions allowing us to pass to the limit with respect to ε. For ε > 0, we consider the following problem :

(P _α ^ε )

( min J ε (y, u, z)

(y, u, z) ∈ K × U _ad × B _R ^∗ , where

J ε (y, u, z) = g ε (y) + h ε (u) + 1

2ε |Ay − Bu − f + z| ² _V

+ 1

2ε [Φ ε (y) + Φ ^∗ _ε (z) − (y, z) _H − α] ² ₊ + 1

2 |y − y α | ² _V + 1

2 |u − u α | ² _U + 1

2 |z − z α | ² _H .

Here g ₊ = max (0, g), and g _ε , h _ε , Φ _ε and Φ ^∗ _ε are the Moreau-Yosida approx- imations of g, h, Φ and Φ ^∗ .

First, we briefly recall some useful properties of Moreau-Yosida approxi- mation of convex functions. Let ϕ be a convex, proper, lower semi continuous function from H to IR ∪ {+∞} where H is an Hilbert space (not necessarily identified with its dual). The Moreau-Yosida approximation of ϕ is defined by

ϕ ε (x) = inf { |x − y| ² _H

2ε + ϕ(y) , y ∈ H }

and we have the following properties ([3] pp.49-55) :

Theorem 4.1. Let us call I ε = (Λ H + εD) ⁻¹ the proximal mapping with D = ∂ϕ, Λ H the canonical isomorphism from H to H ⁰ , and D _ε = −ε ⁻¹ Λ H (I _ε − I).

i. I ε is single valued and non expansive.

ii. D _ε x ∈ ∂ϕ(I _ε x) for all x ∈ H , and for all x ∈ dom(∂ϕ) lim

ε→0 D _ε x = D ^o x ∈ ∂ϕ(x) (strongly in H ⁰ ) where D ^o (x) is the element of minimal norm of ∂ϕ(x).

iii. For all x ∈ dom ϕ I _ε x converges strongly in H towards x.

iv. If ε _n → 0 , x _ε

→ x _o strongly in H and D _ε

x _ε

* y _o weakly in H ⁰ then y o ∈ ∂ϕ(x o ).

v. ϕ _ε is Fr´ echet-differentiable and ϕ ⁰ _ε = D _ε is Lipschitz (so that ϕ _ε is C ¹ ).

vi. For all x ∈ H and ε > 0 ϕ(I _ε x) ≤ ϕ _ε (x) ≤ ϕ(x) . Moreover, for all x ∈ H, lim

ε→0 ϕ ε (x) = ϕ(x) .

In addition, as we need sharper convergence results we set some further as- sumptions about the function ϕ and we suppose that

∀(x _ε ) ∈ dom ϕ strongly convergent (in H ) to x ∈ dom ϕ then ϕ ⁰ _ε (x ε ) is bounded in H ⁰ (with respect to ε).

(4.1)

Then we have the following useful theorem

Theorem 4.2. For any convex, proper, lsc function ϕ, i. If x ε strongly converges to some x in H then lim

ε→0 I ε x ε = x (strongly in H).

ii. If x _ε weakly converges to some x ∈ dom ∂ϕ in H, then lim inf

ε→0 ϕ _ε (x _ε ) ≥ ϕ(x) . iii. If ϕ satisfies condition (4.1) and if x ε ∈ dom ϕ strongly converges to some x ∈ dom ϕ then lim

ε→0 ϕ _ε (x _ε ) = ϕ(x) and x ∈ dom ∂ϕ.

Proof - i. and ii. are direct consequences of Theorem 4.1. To prove iii. we use the relation (2.18) given in Barbu [3] p.66 :

∀z, y ∈ H, ∀ε > 0, ϕ _ε (y) − ϕ _ε (z) ≤ ϕ ⁰ _ε (y), y − z _H

,H

where h , i _H

,H denotes the pairing between H and H ⁰ .

We use it first with z = x _ε and y = x and then with y = x _ε and y = x; this gives

|ϕ _ε (x ε ) − ϕ ε (x)| ≤ max(|ϕ ⁰ _ε (x ε )| _H

, |ϕ ⁰ _ε (x)| _H

)|x − x ε | _H .

With Theorem 4.1 ii., assumption (4.1) and the strong convergence of x _ε to x, we get the strong convergence of ϕ ε (x ε ) to ϕ ε (x). We conclude with Theorem 4.1 vi.

Remark 4.1. The above property is satisfied for any convex, proper, lsc function ϕ as soon as x ∈ int(dom ϕ ) since ∂ϕ(x) is locally bounded in this case (see [4] p. 60). Anyway, here it may happen that int(dom ϕ ) is empty and this result cannot be used.

We precise now the hypotheses on functions Φ and Φ ^∗ ; from now we assume that

Φ satisfies (4.1) with H = V and Φ ^∗ satisfies (4.1) with H = V ⁰ .

(4.2)

This assumption is not so restrictive since it allows to consider a wide class of convex functions; let us give some examples :

Example 4.1. Convex functions satisfying (4.1).

• Any continuous, convex function defined on the whole space V satisfies (4.1) since int(dom ϕ ) = V so that ∂ϕ(x) is locally bounded for any x (we use also Theorem 4.2 i., Theorem 4.1 ii. and a result of Barbu & Precupanu [4] p. 60).

• Any indicator function ϕ = 1 _C of a convex, closed, non empty subset C of H satisfies (4.1) also; we recall that

1 C (y) =

( 0 if y ∈ C +∞ else

and I _ε (x) = P _C (x) where P _C is the H-projection on C. Then ϕ ε (x) = |x − P C (x)| ² _H

2ε and ϕ ⁰ _ε (x) = Λ H

x − P C (x) ε

.

If x ε strongly converges to x in C =dom ϕ, then ϕ ⁰ _ε (x ε ) = 0 for all ε and so remains bounded in H ⁰ .

Example 4.2. Convex functions satisfying (4.2).

• If Φ is the indicator function of a convex closed cone C of V then Φ ^∗ = 1 _C

where C ^∗ is the polar cone of C in V ⁰ ; so with Example 4.1. we see that (4.2) is ensured.

This case involves the obstacle problem or the Signorini problem.

• If Φ(x) = |x| _V is continuous and dom (Φ) = H then Φ ^∗ is the indicator function of the unit ball of V ⁰ .

• Φ(x) = ¹ _p |x| ^p _V then Φ ^∗ (x) = _p ¹

|x| ^p _V

where p, p ⁰ ∈]1, +∞[ are conjugate num- bers (see Ekeland-Temam [10]). This leads to a semilinear state-equation.

Remark 4.2. The approximation process concerns the functions g, h, Φ and Φ ^∗ which are not necessarily Fr´ echet-differentiable and are replaced by their Moreau-Yosida approximations. This method provides C ¹ functions.

We have also added two kinds of penalization terms : the state-equation and the inequality (non convex) constraint are penalized in a standard way. The other terms are adapted penalization terms which ensure the strong convergence of the penalized solution towards the desired solution (when uniqueness does not hold).

First we have an existence and convergence result for (P _α ^ε ).

Theorem 4.3. For all ε > 0, problem (P _α ^ε ) has (at least) a solution (y _ε , u _ε , z _ε ). Moreover, when ε → 0, (y _ε , u _ε , z _ε ) → (y _α , u _α , z _α ) strongly in V × U × H.

Proof - We first prove the existence of a solution for (P _α ^ε ). We notice that

(y α , u α , z α ) is a feasible triple for (P _α ^ε ) so that the feasible domain of (P _α ^ε ) is

non empty and we may find a minimizing sequence (y _ε ⁿ , u ⁿ _ε , z _ε ⁿ ) converging to

d _ε = inf(P _α ^ε ). Setting I _ε,g = (I _H + ε∂g) ⁻¹ and I _ε,h = (I _U + ε∂h) ⁻¹ we get J ε (y, u, z) ≥ g ε (y) + h ε (u) ≥ g(I ε,g (y)) + h(I ε,h (u)) , and

V ×U×H inf J _ε (y, u, z) ≥ inf

V ×U g(I _ε,g (y)) + h(I _ε,h (u)) ≥ γ > −∞

because of the properties of g and h. So d _ε ∈ IR and the end of the proof is standard (see Theorem 3.1).

Now we prove the convergence result. Since (y _α , u _α , z _α ) is a feasible triple for (P _α ^ε ) we have

d _ε = J _ε (y _ε , u _ε , z _ε ) ≤ g(y _α ) + h(u _α ) = d _α . (4.3)

We have just seen that d _ε is lower bounded (with respect to ε) so that y ε , u ε and z ε are bounded in V, U and H. Extracting a subsequence, we get the weak convergence of (y _ε , u _ε , z _ε ) to (˜ y, u, ˜ z) in ˜ V × U × H; in particular, this yields that Ay _ε − Bu _ε − f + z _ε converges to A˜ y − B u ˜ − f + ˜ z weakly in V ⁰ . Moreover Ay _ε − Bu _ε − f + z _ε converges to 0 strongly in V ⁰ so that A˜ y − B u ˜ − f + ˜ z = 0 . In addition, as U _ad , K and B _R ^∗ are weakly closed we get

˜

u ∈ U _ad , y ˜ ∈ K and ˜ z ∈ B _R ^∗ .

The injection of V in H is compact, so y _ε → y ˜ strongly in H; as z _ε * z ˜ weakly in H we get the convergence of (y _ε , z _ε ) _H to (˜ y, z) ˜ _H . Moreover Theorem 4.2 gives

lim inf

ε→0 Φ _ε (y _ε ) ≥ Φ(˜ y) and lim inf

ε→0 Φ ^∗ _ε (z _ε ) ≥ Φ ^∗ (˜ z) . So we get

[Φ(˜ y) + Φ ^∗ (˜ z) − (˜ y, z) ˜ _H − α] + ≤ lim inf

ε→0 [Φ ε (y ε ) + Φ ^∗ _ε (z ε ) − (y ε , z ε ) _H − α] + ; Since lim

ε→0 [Φ ε (y ε ) + Φ ^∗ _ε (z ε ) − (y ε , z ε ) _H − α] ² ₊ = 0 this yields [Φ(˜ y) + Φ ^∗ (˜ z) − (˜ y, z) ˜ _H − α] + = 0 . So (˜ y, u, ˜ z) is feasible for (P ˜ _α ). Now relation (4.3) gives

g _ε (y _ε ) + h _ε (u _ε ) + 1

2 |y _ε − y _α | ² _V + 1

2 |u _ε − u _α | ² _U + 1

2 |z _ε − z _α | ² _H ≤ g(y _α ) + h(u _α ) . Passing to the inf-limit in the above relation we get

g(˜ y)+h(˜ u)+ 1

2 |˜ y−y _α | ² _V + 1

2 |˜ u−u _α | ² _U + 1

2 |˜ z−z _α | ² _H ≤ g(y α )+h(u α ) ≤ g(˜ y)+h(˜ u) , since (˜ y, u, ˜ z) is feasible for (P ˜ _α ). So ˜ y = y _α , u ˜ = u _α and ˜ z = z _α . Furthermore

ε→0 lim |y _ε − y α | _V = 0, lim

ε→0 |u _ε − u α | _U = 0 and lim

ε→0 |z _ε − z α | _H = 0 and we get the

strong convergence.

Corollary 4.1. There exists (y ^∗ , z ^∗ ) ∈ ∂Φ ^∗ (z _α ) × ∂Φ(y _α ) such that Φ ⁰ _ε (y ε ) * z ^∗ weakly in V ⁰ and Φ ^∗ _ε

(z ε ) * y ^∗ weakly in V (and strongly in H).

Moreover

ε→0 lim

Φ ⁰ _ε (y _ε ), y _ε = hz ^∗ , y _α i and lim

ε→0

Φ ^∗ _ε

(z _ε ), z _ε

H = (y ^∗ , z _α ) _H . (4.4)

Proof - As y _ε ∈ K ⊂ dom(Φ) strongly converges to y _α in V , we use assumption (4.2) to infer that Φ ⁰ _ε (y _ε ) is bounded in V ⁰ . So we may extract a subsequence (denoted similarly) such that Φ ⁰ _ε (y ε ) weakly converges in V ⁰ to z ^∗ . Theorem 4.1 iv. implies that z ^∗ ∈ ∂Φ(y α ). Similarly, we may prove that Φ ^∗ _ε

(z ε ) * y ^∗ ∈

∂Φ ^∗ (z _α ) weakly in V and strongly in H, since z _ε strongly converges to z _α in V ⁰ . Relations (4.4) are obvious.

4.2. Optimality Conditions for (P _α ^ε ). Now, we want to derive opti- mality conditions for (P _α ^ε ). J _ε is C ¹ and the feasible domain of (P _α ^ε ) is convex, so using convex variations we have

∀(y, u, z) ∈ K × U _ad × B _R ^∗ ∇J _ε (y ε , u ε , z ε )(y − y ε , u − u ε , z − z ε ) ≥ 0 . (4.5)

This leads to the following penalized optimality system :

Theorem 4.4. For all ε > 0 (small enough), there exist q ε ∈ V and λ _ε ∈ IR ⁺ such that

∀y ∈ K

(g _ε ⁰ (y ε ), y − y ε ) _H +(y ε − y α , y −y _ε ) _V +hA ^∗ q ε +λ ε [Φ ⁰ _ε (y ε )− z ε ], y−y _ε i ≥ 0 (4.6)

∀u ∈ U ad h ⁰ _ε (u ε ) − B ^∗ q ε + u ε − u α , u − u ε

U ≥ 0 , (4.7)

∀z ∈ B _R ^∗ q ε + λ ε [Φ ^∗ _ε

(z ε ) − y ε ] + z ε − z α , z − z ε

H ≥ 0 , (4.8)

where A ^∗ and B ^∗ are the adjoint operators of A and B.

Proof - Relation (4.5) may be decoupled to obtain

∀y ∈ K ∇ _y J _ε (y _ε , u _ε , z _ε )(y − y _ε ) ≥ 0 , (4.9)

∀u ∈ U _ad ∇ _u J ε (y ε , u ε , z ε )(u − u ε ) ≥ 0 , (4.10)

∀z ∈ B ^∗ _R ∇ _z J ε (y ε , u ε , z ε )(z − z ε ) ≥ 0 . (4.11)

Let us precise these relations : setting q _ε = Λ ⁻¹ _V (s _ε ) ∈ V , and s ε = Ay ε − Bu ε − f + z ε

ε ∈ H ⊂ V ⁰ , λ ε = [Φ ε (y ε ) + Φ ^∗ _ε (z ε ) − (y ε , z ε ) _H − α] +

ε ∈ IR ⁺

relation (4.9) gives, for all y ∈ K

(g ⁰ _ε (y _ε ), y − y _ε ) _H + (y _ε − y _α , y − y _ε ) _V +

hλ _ε [Φ ⁰ _ε (y ε ) − z ε ], y − y ε i + hq _ε , A(y − y ε )i ≥ 0 ;

introducing the adjoint operator A ^∗ of A we get (4.6). The other relations are obtained similarly.

Remark 4.3. Equation (4.8) (and (4.7) as well) can be reformulated using the normal cone to B ^∗ _R . Indeed, as B _R ^∗ is convex this normal cone is characterized with

N _B

R

(z _ε ) = { ξ ∈ H | (ξ, z _ε − z) _H ≥ 0 ∀z ∈ B _R ^∗ }

(see for instance Clarke [9] p.53 ), so that relation (4.8) is equivalent to

−[q _ε + λ _ε (Φ ^∗ _ε

(z _ε ) − y _ε )] ∈ z _ε − z _α + N _B

R

(z _ε ) . (4.12)

5. Optimality Conditions for (P _α ). In order to pass to the limit (with respect to ε) in the previous relations we need further estimations on the multipliers q _ε and λ _ε .

5.1. Estimations of the penalized multipliers. Let be (y, u, z) ∈ K × U ad × B ^∗ _R and let us add relations (4.6)-(4.8). This gives

(g _ε ⁰ (y ε ), y − y ε ) _H + (h ⁰ _ε (u ε ), u − u ε ) _U +

(y _ε − y _α , y − y _ε ) _V + (u _ε − u _α , u − u _ε ) _U + (z _ε − z _α , z − z _ε ) _H + hq _ε , Ay − Bu + z − f − (Ay _ε − Bu _ε + z _ε − f )i +

λ _ε ^h hΦ ⁰ _ε (y _ε ) − z _ε , y − y _ε i + Φ ^∗ _ε

(z _ε ) − y _ε , z − z _ε

H

i ≥ 0 . Using the definition of q _ε and that ε hq _ε , Λ _V q _ε i ≥ 0 we get

h−q _ε , Ay−Bu + z − f i − λ _ε ^h hΦ ⁰ _ε (y _ε ) −z _ε , y − y _ε i + Φ ^∗ _ε

(z _ε ) − y _ε , z − z _ε

H

i

≤

(g _ε ⁰ (y ε ), y − y ε ) _H + (h ⁰ _ε (u ε ), u − u ε ) _U

+ (y _ε − y _α , y − y _ε ) _V + (u _ε − u _α , u − u _ε ) _U + (z _ε − z _α , z − z _ε ) _H .

The right-hand side term is bounded since (y ε , u ε , z ε ) → (y α , u α , z α ) strongly in V × U × H, and (g _ε ⁰ (y _ε ), h ⁰ _ε (u _ε )) * (y _α ^g , u ^h _α ) ∈ ∂g(y _α ) × ∂h(u _α ) weakly in H × U (because of Theorem 4.2 iii. and the continuity of g and h). The bounding constant σ depends only of (y, u, z). So we have, for all ε > 0 small enough,

−λ _ε ^h hΦ ⁰ _ε (y _ε ) − z _ε , y − y _ε i + Φ ^∗ _ε

(z _ε ) − y _ε , z − z _ε

H

i

− hq _ε , Ay − Bu + z − f i ≤ σ(y, u, z) . (5.1)

We first estimate the real number λ _ε . If the solution (y _α , u _α , z _α ) is such that the non-convex constraint is inactive, i.e.

Φ(y α ) + Φ ^∗ (z α ) − (y α , z α ) _H − α = G(y α , z α ) < 0 ,

then convergence results yield

∃ε _o > 0, ∀ε ≤ ε _o , Φ _ε (y _ε ) + Φ ^∗ _ε (z _ε ) − (y _ε , z _ε ) _H − α < 0 , as well and λ ε = 0; hence the limit λ α = 0.

Now, we investigate the case when the constraint is active that is Φ(y _α ) + Φ ^∗ (z _α ) − (y _α , z _α ) _H − α = G(y _α , z _α ) = 0 . Let us assume the following condition (H ₁ ) :

(H ₁ ) ∀α such that G(y α , z α ) = 0, ∀(y ^∗ , z ^∗ ) ∈ ∂Φ ^∗ (z α ) × ∂Φ(y α )

∃(˜ y, u, ˜ z) ˜ ∈ K × U _ad × B _R ^∗ such that A˜ y = B u ˜ + f − z ˜ and [Φ(y _α ) − Φ(y ^∗ ) − hy _α − y ^∗ , zi] + [Φ ˜ ^∗ (z _α ) − Φ ^∗ (z ^∗ ) − hz _α − z ^∗ , yi] ˜ < 2α.

(5.2)

Remark 5.1. Relation (5.2) is indeed equivalent to

hy _α − y ^∗ , z ˜ − z α i + hz _α − z ^∗ , y ˜ − y α i > 0

as we shall prove it later. Moreover, in our very case, hy _α − y ^∗ , zi ˜ = (y _α − y ^∗ , z) ˜ _H since z ˜ ∈ H.

Theorem 5.1. Assume (H ₁ ); then λ _ε is bounded by a constant indepen- dent of ε and we may extract a subsequence converging to λ α ∈ IR ⁺ .

Proof - If α is such that G(y _α , z _α ) < 0 we have already seen that λ _α = 0.

If G(y _α , z _α ) = 0, we use (H ₁ ). Let be (y ^∗ , z ^∗ ) ∈ ∂Φ ^∗ (z _α ) × ∂Φ(y _α ) ⊂ V × V ⁰ given by Corollary 4.1. Let us apply relation (5.1) with the triple (˜ y, u, ˜ z), ˜ given by (H 1 ). We get

λ _ε ^h z _ε − Φ ⁰ _ε (y _ε ), y ˜ − y _ε + y _ε − Φ ^∗ _ε

(z _ε ), z ˜ − z _ε

H

i ≤ C ˜ (5.3)

and

Φ(y _α ) − Φ(y ^∗ ) − (y _α − y ^∗ , z) ˜ _H + Φ ^∗ (z _α ) − Φ ^∗ (z ^∗ ) − hz _α − z ^∗ , yi ˜ < 2α.

As y ^∗ ∈ ∂Φ ^∗ (z _α ) and z ^∗ ∈ ∂Φ(y _α ) we have Φ(y ^∗ ) + Φ ^∗ (z _α ) = (y ^∗ , z _α ) _H and Φ ^∗ (z ^∗ ) + Φ(y _α ) = hz ^∗ , y _α i.

Moreover we are in the case where Φ(y _α ) + Φ ^∗ (z _α ) = (y _α , z _α ) _H + α, so that (5.2) is equivalent to

ρ = (y _α − y ^∗ , z ˜ − z _α ) _H + hz _α − z ^∗ , y ˜ − y _α i > 0 , as mentioned in Remark 5.1.

Convergence results given in Theorem 4.3 and Corollary 4.1 imply that

ε→0 lim

y _ε − Φ ^∗ _ε

(z _ε ), z ˜ − z _ε

H + z _ε − Φ ⁰ _ε (y _ε ), y ˜ − y _ε = ρ > 0 .

So, there exists ε _o > 0 such that for all 0 < ε < ε _o we have y _ε − Φ ^∗ _ε

(z _ε ), z ˜ − z _ε

H + z _ε − Φ ⁰ _ε (y _ε ), y ˜ − y _ε ≥ ρ 2 . Then relation (5.3) gives

∀ε < ε _o 0 ≤ ρ

2 λ ε ≤ C . ˜

So λ _ε is bounded and converges to some λ _α ∈ IR ⁺ (extracting a subsequence).

It remains to bound q _ε . Following [7] we assume the (qualification) condition (H ₂ ) :

(H ₂ )



 

 

 

 

 

 

∃W separable Banach subspace such that W ⊂ V ⁰ continuously and densely ,

∃M ⊂ K × U _ad × B _R ^∗ bounded in V × U × H, such that 0 ∈ Int W T (M) in W -topology

where T (y, u, z) = Ay − Bu − f + z .

More precisely, 0 ∈ Int _W T (M) means the existence of ρ > 0 such that

∀ξ ∈ W, |ξ| _W ≤ 1, ∃(y _ξ , u ξ , z ξ ) ∈ M such that Ay ξ = Bu ξ + f − z ξ + ρξ . Theorem 5.2. Assume (H ₁ ) and (H ₂ ); then q _ε is bounded in W ⁰ and one may extract a subsequence converging weak* to q α in W ⁰ .

Proof - Let be ρ > 0 given by (H 2 ) and ξ ∈ W such that |ξ| _W ≤ 1. We use relation (5.1) with (y _ξ , u _ξ , z _ξ ) and we get

h−q _ε , ρξi ≤ C 1 + C 2 λ ε ,

where C ₁ and C ₂ are constants dependent only of (y _ξ , u _ξ , z _ξ ). Assumption (H 1 ) provides a bound for λ ε and M is bounded. So there exists a constant C (depending only of M) such that

∀ξ ∈ W, |ξ| _W ≤ 1 hq _ε , ξi _W

,W ≤ C ;

(as W ⊂ V ⁰ and q _ε ∈ V then q _ε ∈ W ⁰ ). Thus q _ε is bounded in W ⁰ .

Now, we are able to pass to the limit in the penalized optimality system with respect to ε.

5.2. Optimality Conditions for (P _α ).

Theorem 5.3. Let be α > 0 and assume (H 1 ) and (H 2 ); then there exists (y _α ^g , u ^h _α , z ^∗ _α , y _α ^∗ ) ∈ ∂g(y _α ) × ∂h(u _α ) × ∂Φ(y _α ) × ∂Φ ^∗ (z _α ) ⊂ H × U × V ⁰ × V and (q _α , λ _α ) ∈ W ⁰ × IR ⁺ such that

∀y ∈ K such that A(y − y _α ) ∈ W

(y _α ^g , y − y α ) _H + hq _α , A(y − y α )i _W

,W + λ α hz _α ^∗ − z α , y − y α i _V

,V ≥ 0 ,

(5.4)

∀u ∈ U _ad such that B(u − u _α ) ∈ W u ^h _α , u − u α

U − hq _α , B(u − u α )i _W

,W ≥ 0 , (5.5)

∀z ∈ B _R ^∗ such that z − z α ∈ W

hq _α , z − z α i _W

,W + λ α (y ^∗ _α − y α , z − z α ) _H ≥ 0 , (5.6)

λ α [Φ(y α ) + Φ ^∗ (z α ) − (y α , z α ) _H − α] = 0 . (5.7)

Proof - Let be y ∈ K such that A(y −y _α ) ∈ W, u ∈ U ad such that B(u −u _α ) ∈ W and z ∈ B _R ^∗ such that z − z α ∈ W . We use relations (4.6-4.8) with these test functions and add them to get :

(g _ε ⁰ (y _ε ), y − y _ε ) _H + (h ⁰ _ε (u _ε ), u − u _ε ) _U +

(y _ε − y _α , y − y _ε ) _V + (u _ε − u _α , u − u _ε ) _U + (z _ε − z _α , z − z _ε ) _H + hq _ε , Ay − Bu + z − f − (Ay ε − Bu ε + z ε − f )i +

λ _ε ^h hΦ ⁰ _ε (y _ε ) − z _ε , y − y _ε i + Φ ^∗ _ε

(z _ε ) − y _ε , z − z _ε

H

i ≥ 0 , that is, as subsection 2.1,

(g _ε ⁰ (y _ε ), y − y _ε ) _H + (h ⁰ _ε (u _ε ), u − u _ε ) _U + (u _ε − u _α , u − u _ε ) _U + (z _ε − z _α , z − z _ε ) _H + hq _ε , A(y − y α ) − B (u − u α ) + z − z α i _W

,W + λ _ε ^h hΦ ⁰ _ε (y _ε ) − z _ε , y − y _ε i + Φ ^∗ _ε

(z _ε ) − y _ε , z − z _ε

H

i ≥ 0 .

As g and h are continuous and finite everywhere, (g _ε ⁰ (y ε ), h ⁰ _ε (u ε )) converges towards some (y ^g _α , u ^h _α ) ∈ ∂g(y α ) × ∂h(y α ). Then we may pass to the limit in the above relation to infer

(y ^g _α , y − y _α ) _H + u ^h _α ), u − u _α

U +

hq _α , A(y − y α ) − B(u − u α ) + z − z α i _W

,W +

λ α [hz _α ^∗ − z α , y − y α i + (y ^∗ α − y α , z − z α ) _H ] ≥ 0 .

Taking in turn y = y α , u = u α and z = z α we obtain relations (5.4)-(5.6).

Finally if Φ(y _α ) + Φ ^∗ (z _α ) − (y _α , z _α ) _H − α < 0 we have seen that λ _α = 0. So relation (5.7) is satisfied.

Remark 5.2. As we already mentioned it in Remark 4.3, equation (5.6) is equivalent to

−q _α − λ _α (y ^∗ _α − y _α ) ∈ N _B

R

∩(z

+W) (z _α ) . (5.8)

Corollary 5.1. With assumptions of Theorem 5.3, there exists (y _α ^g , u ^h _α ) ∈

∂g(y _α ) × ∂h(u _α ) ⊂ H × U and (q _α , λ _α ) ∈ W ⁰ × IR ⁺ such that

∀y ∈ K s.t. A(y − y _α ) ∈ W

(y _α ^g , y − y _α ) _H + hq _α , A(y − y _α )i _W

,W +

λ α [Φ(y) + Φ ^∗ (z α ) − (y, z α ) _H − α] ≥ 0,

(5.9)

∀u ∈ U _ad s.t. B(u − u _α ) ∈ W u ^h _α , u − u _α

U − hq _α , B(u − u _α )i _W

,W ≥ 0 ,

∀z ∈ B _R ^∗ s.t. z − z α ∈ W

hq _α , z − z _α i _W

,W + λ _α [Φ(y _α ) + Φ ^∗ (z) − (y _α , z) _H − α] ≥ 0 , (5.10)

λ _α [Φ(y _α ) + Φ ^∗ (z _α ) − (y _α , z _α ) _H − α] = 0 .

Proof - Theorem 5.3 gives (z ^∗ _α , y _α ^∗ ) ∈ ∂Φ(y α ) × ∂Φ ^∗ (z α ) ⊂ V ⁰ × V such that relations (5.4) and (5.6) are satisfied.

As z _α ^∗ ∈ ∂Φ(y α ) we get, for all y ∈ K such that A(y − y α ) ∈ W Φ(y) − Φ(y α ) ≥ hz ^∗ _α , y − y α i ,

so that relation (5.4) becomes

(y _α ^g , y − y α ) _H + hq _α , A(y − y α )i _W

,W + λ α [Φ(y) − Φ(y α ) − (z α , y − y α ) _H ] ≥ 0 . Using (5.7) we obtain relation (5.9). We can show similarly relation (5.10).

Corollary 5.2. With assumptions of the previous theorem and if g and h are Gˆ ateaux-differentiable, there exists (q α , λ α ) ∈ W ⁰ × IR ⁺ such that

∀y ∈ K s.t. A(y − y _α ) ∈ W

(g ⁰ (y α ), y − y α ) _H + hq _α , A(y − y α )i _W

,W + λ α [Φ(y) + Φ ^∗ (z α ) − (z α , y) _H − α] ≥ 0 ,

∀u ∈ U ad s.t. B(u−u _α ) ∈ W h ⁰ (u α ), u − u α

U −hq _α , B(u − u α )i _W

,W ≥ 0,

∀z ∈ B _R ^∗ s.t. z − z _α ∈ W

hq _α , z − z _α i _W

,W + λ _α [Φ(y _α ) + Φ ^∗ (z) − (z, y _α ) _H − α] ≥ 0 , λ α [Φ(y α ) + Φ ^∗ (z α ) − (y α , z α ) _H − α] = 0 .

Remark 5.3. The natural idea would be now to study the asymptotic behavior of the previous optimality system when α → 0. Unfortunately, we would have to set an “(H1)-like” assumption with α = 0, to be able to pass to the limit in the α-optimality system. This is impossible since the interior of the feasible domain of P is empty because of the non convex equality constraint and an assumption like (H1) with α = 0 would be never ensured. However, as we have already mentioned, this relaxed approach is sufficient for numerical applications.

6. Example of The Obstacle Problem. In this section, we study an example where the variational inequality leads to an obstacle problem.

Let Ω be an open, bounded subset of IR ⁿ with a smooth boundary ∂Ω. We

consider a bilinear form a(., .) defined on H _o ¹ (Ω)×H _o ¹ (Ω) and A the continuous

linear operator from H _o ¹ (Ω) to H ⁻¹ (Ω) associated to a such that



 

 

 

 

 

 

 

 

Ay = −

n

X

i,j=1

∂ _x

(a _ij (x)∂ _x

y) + a ₀ (x)y with

a ij , a 0 ∈ C ² ( ¯ Ω) f or i, j = 1, . . . , n, inf {a ₀ (x) | x ∈ Ω} ¯ > 0

n

X

ij=1

a ij (x)ξ i ξ j ≥ δ

n

X

i=1

ξ _i ² , ∀x ∈ Ω, ¯ ∀ξ ∈ IR ⁿ , δ > 0 , (6.1)

We shall denote k k, the L ² (Ω)-norm, ( , ) the L ² (Ω)-scalar product and h , i any duality-product. We set

V = H _o ¹ (Ω) , H = L ² (Ω) , D H (A) = H ² (Ω)∩H _o ¹ (Ω) , U = L ² (Ω) and B = Id _L

_(Ω) . Let us set also

K = V and C ⁺ = {y | y ∈ H _o ¹ (Ω) , y ≥ 0 a.e. in Ω} .

The convex function Φ is the indicator function I ₊ of C ⁺ , Then Φ ^∗ is the indicator function I − of the negative cone C ⁻ of H ⁻¹ and we have already mentioned that Φ and Φ ^∗ satisfy condition (4.2). Then we get as a state equation

Ay = f + v − z in Ω, y = 0 on Γ , (6.2)

with f, v and z belong to L ² (Ω) (because of the regularity result mentioned in Section 1.) The constraint z ∈ ∂Φ(y) becomes

y ≥ 0 , z ≤ 0 , (y, z) = 0 ,

and the α-inequality constraint Φ(y) + Φ ^∗ (z) − (y, z ) ≤ α gives : y ≥ 0 , z ≤ 0 , (y, −z) ≤ α .

We set ξ = −z, so that the original control problem is defined as follows (see [5])

min

J (y, v) = 1 2

Z

Ω

(y − z _d ) ² dx + M 2

Z

Ω

v ² dx

, (P )

Ay = f + v + ξ in Ω , y = 0 on Γ , (6.3)

(y, v, ξ) ∈ D, (6.4)

where

D = {(y, v, ξ) ∈ H _o ¹ (Ω) × L ² (Ω) × L ² (Ω) | v ∈ U _ad , y ≥ 0, ξ ≥ 0, (y, ξ) = 0}

and z d ∈ L ² (Ω); The relaxed problem is

(P ^α ) min J(y, v),

Ay = v + ξ in Ω, y ∈ H _o ¹ (Ω), (6.5)

(y, v, ξ) ∈ D ^R _α , (6.6)

where

D ^R _α = {(y, v, ξ) ∈ H _o ¹ (Ω)×L ² (Ω)×L ² (Ω) | v ∈ U ad , y ≥ 0, ξ ≥ 0, kξk ≤ R, (y, ξ) ≤ α}.

The results of the previous section may be applied with W = L ^p (Ω) and we get :

Theorem 6.1. Assume

∀α such that hy _α , ξ α i = α ,

∃(˜ y, v, ˜ ξ) ˜ ∈ C ⁺ × U _ad × B _R ^∗ such that A y ˜ = ˜ v + ˜ ξ and (˜ y, ξ α ) + y α , ξ ˜ < 2α .

(H ₁ )

and

∃p ∈ [1, +∞[, ∃ ρ > 0 , ∀ χ ∈ L ^p (Ω), kχk _L

(Ω) ≤ 1 ,

∃(y _χ , v χ , ξ χ ) bounded in C ⁺ × U ad × B _R ^∗ (independently of χ) , such that Ay χ = v χ + ξ χ + ρχ in Ω.

(H ₂ ) and let (y _α , v _α , ξ _α ) be a solution of (P ^α ); then a lagrange multiplier (q _α , λ _α ) ∈ L ^p

(Ω) × IR ⁺ exists, such that

∀y ∈ C ⁺ such that A(y − y _α ) ∈ L ^p (Ω)

(y α − z d , y − y α ) + hq _α , A(y − y α )i + λ α (ξ α , y − y α ) ≥ 0 , (6.7)

∀v ∈ U ad , v − v α ∈ L ^p (Ω) hM v _α − q α , v − v α i ≥ 0 , (6.8)

∀ξ ∈ B _R ^∗ , ξ − ξ α ∈ L ^p (Ω) hλ _α y α − q α , ξ − ξ α i ≥ 0 , (6.9)

λ α ((y α , ξ α ) − α) = 0 . (6.10)

For more details one can refer to [5]. We just mention that assumptions (H ₁ ) and (H ₂ ) are satisfied for instance if U _ad = L ² (Ω) or U _ad = { v ∈ L ² (Ω) | v ≥ ψ ≥ 0 a.e. in Ω}.

7. Conclusion. As already mentioned at the beginning of this paper, we have in mind the numerical aspects of the question: that is why we have underlined that the “relaxed” problem P _α is a good approximation of the original problem. Now, we think that the main tool for a good numerical approach for such problems is the (necessary) optimality conditions that we have obtained in Theorem 5.3. They allow to interpret the optimal solution as the first argument of the saddle-point of a linearized lagrangian function, though the problem is not convex. We have developed this point of view and presented some algorithms in [6], for the case of the obstacle problem. The numerical behavior of these methods is quite nice.

On the other hand, though we have not tested methods using Yosida ap- proximation, we believe that the use of penalization is not helpful for numerics.

It seems to be too much unstable (because of the suitable choice of the pa-

rameter ε) and we think is is only a theoretical tool.

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