#### Introduction

We introduce the following notation: , ,

and the correspondent norms and . Moreover, we define the norms

We want to study the following optimal control problem:

where is defined by

and is the solution of the PDE given by

Notice that, by integration by parts, , where is solution of the adjoint equation:

#### Sparse control: ()

##### The stationary problem

###### Optimality conditions

###### Numerical algorithm

In order to compute a numerical solution of problem , after a discretization by finite differences, we use a prox-prox splitting: first write the state as , then

- Proximal-point step:
- Proximal-point step:

**Remark:** Notice that, when , the solution of is simply given by

##### Evolutionary problem

###### Optimality conditions

Define the classical Lagrangian

By integration by parts, we have

Deriving with respect to the three variables , we obtain the optimality system:

where the relation between the optimal control and the dual state is given by

The latter is equivalent to

where the operator of is defined by

Finally,

###### Numerical algorithm

In order to compute a numerical solution of problem , after a discretization by finite differences, we use a grad-prox splitting:

- Gradient step:
- Proximal-point step:

where

and

**Remarks:** Another possibility is to include the term in the proximal step.

Notice that, for

then is Lipschitz continuous. Indeed, for (), then

where

and

By linearity and solve the same equations with right-hand-sides and , respectively. Then

where we defined

In order the prox-grad method to converge, the restriction on the step size is given by

#### Sparse state: ()

##### The stationary problem

###### Optimality conditions

Finally, we obtain a single equation in the dual variable :

###### Numerical algorithm

In order to compute a numerical solution of problem , after a discretization by finite differences, we use a prox-prox splitting on the Augmented Energy: first write the state as , then

- Proximal-point step:
- Proximal-point step:

where we defined

**Remark:** Notice that again, when , the solution of is simply given by

##### Evolutionary problem

###### Optimality conditions

Define the classical Lagrangian

By integration by parts, we have

Deriving with respect to the three variables , we obtain the optimality system:

where the relation between the optimal control and the dual state is given by

The adjoint equation is equivalent to

Finally,

###### Numerical algorithm

In order to compute a numerical solution of problem , after a discretization by finite differences, we use a grad-prox splitting on the following Augmented Energy:

Then,

- Gradient step:
- Proximal-point step:
- Spacial domain: ;
- Time interval: , with ;
- Weight-parameters: , , and ;
- Trajectory target:
where , ;

- Control operator: for and ,
- is the finite difference discretization of ;
- Numerical grid: in space, in time.

where

and

where we defined

**Remark:**Another possibility is to consider

#### Computational experiments

In the following, we present the setting for the numerical experiments.

##### Stationary solutions

##### Evolutionary problem

###### Optimal control

###### Optimal state

###### Optimal adjoint

###### Bibliography

**[1]** Peypouquet, J. *Convex optimization in normed spaces: theory, methods and examples. With a foreword by Hedy Attouch.* Springer Briefs in Optimization. Springer, Cham, 2015. xiv+124 pp.