Turnpike property for functionals involving L norm · 2021. 2. 10. · June 15, 2017 Optimization...

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June 15, 2017 Optimization ”Turnpike L1-norm” To appear in Optimization Vol. 00, No. 00, Month 20XX, 1–20 Turnpike property for functionals involving L 1 -norm ? (Received 00 Month 20XX; accepted 00 Month 20XX) Keywords: optimal control; parabolic equations; convex optimization AMS Subject Classification: 49M05; 90C30 1. Introduction We introduce the following notation: L 2 = L 2 (Ω), L 2 T = L 2 × (0,T )), hu, vi = Z Ω u(x)v(x) dx; hu, vi 2 T = Z T 0 Z Ω u(x, t)v(x, t) dx dt and the correspondent norms k·k = , ·i and k·k T = , ·i T . Moreover, we define the norms kvk 1 = Z Ω | v(x) | dx; kvk 1,T = Z T 0 Z Ω | v(x, t) | dx dt. We want to study the following optimal control problem: (P ) ˆ u arg min uL 2 T J (u)= α c kuk 1,T + β 2 kuk 2 T + α s kLuk 1,T + γ 2 kLu - z k 2 T , where L : L 2 T L 2 T is defined by Lu = y ? This research was partially developed while the first two authors were visiting the BCAM under MINECO Grant MTM2011-29306. It was also partly supported by Croatian Science Foundation under Project WeConMApp/HRZZ-9780, University of Dubrovnik through the Erasmus+ Programme, Fondecyt Grant 1140829, Conicyt Anillo ACT-1106, ECOS Project C13E03, Millenium Nucleus ICM/FIC RC130003, MATH-AmSud Project 15MATH-02, Conicyt Redes 140183, and Basal Project CMM Universidad de Chile. 1

Transcript of Turnpike property for functionals involving L norm · 2021. 2. 10. · June 15, 2017 Optimization...

Page 1: Turnpike property for functionals involving L norm · 2021. 2. 10. · June 15, 2017 Optimization "Turnpike L1-norm" 2.2.2. Numerical algorithm In order to compute a numerical solution

June 15, 2017 Optimization ”Turnpike L1-norm”

To appear in OptimizationVol. 00, No. 00, Month 20XX, 1–20

Turnpike property for functionals involving L1−norm?

(Received 00 Month 20XX; accepted 00 Month 20XX)

Keywords: optimal control; parabolic equations; convex optimization

AMS Subject Classification: 49M05; 90C30

1. Introduction

We introduce the following notation: L2 = L2 (Ω), L2T = L2 (Ω× (0, T )),

〈u, v〉 =

∫Ωu(x)v(x) dx;

〈u, v〉2T =

∫ T

0

∫Ωu(x, t)v(x, t) dx dt

and the correspondent norms ‖ · ‖ = 〈·, ·〉 and ‖ · ‖T = 〈·, ·〉T . Moreover, we definethe norms

‖v‖1 =

∫Ω| v(x) | dx;

‖v‖1,T =

∫ T

0

∫Ω| v(x, t) | dx dt.

We want to study the following optimal control problem:

(P) u ∈ arg minu∈L2

T

J (u) = αc‖u‖1,T +

β

2‖u‖2T + αs‖Lu‖1,T +

γ

2‖Lu− z‖2T

,

where L : L2T → L2

T is defined by

Lu = y

? This research was partially developed while the first two authors were visiting the BCAM under

MINECO Grant MTM2011-29306. It was also partly supported by Croatian Science Foundation under

Project WeConMApp/HRZZ-9780, University of Dubrovnik through the Erasmus+ Programme, FondecytGrant 1140829, Conicyt Anillo ACT-1106, ECOS Project C13E03, Millenium Nucleus ICM/FIC RC130003,MATH-AmSud Project 15MATH-02, Conicyt Redes 140183, and Basal Project CMM Universidad de Chile.

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and y is the solution of the PDE given byy′ +Ay = Bu (Ω× (0, T ))

y = 0 (∂Ω× (0, T ))

y(0) = 0 (Ω) .

Notice that, by integration by parts, L∗µ = B∗p, where ϕ is solution of the adjointequation:

−p′ +A∗p = µ (Ω× (0, T ))

p = 0 (∂Ω× (0, T ))

p(T ) = 0 (Ω) .

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2. Sparse control: αc > 0 (αs = 0)

2.1. The stationary problem

(SPc) u ∈ arg minu∈L2

Js (u) = αc‖u‖1 +

β

2‖u‖2 +

γ

2‖y − z‖2 : Ay = Bu

.

2.1.1. Optimality conditions

Ay = B shrink(−B∗p, αc

β ) (Ω)

A∗p = γ (y − z) (Ω)

y = 0, p = 0 (∂Ω) .

2.1.2. Numerical algorithm

In order to compute a numerical solution of problem (SPc), after a discretization byfinite differences, we use a prox-prox splitting: first write the state as y = A−1Bu,then

• Proximal-point step:

uk = arg minu∈L2

β

2‖u‖2 +

γ

2‖A−1Bu− z‖2 +

1

2λk‖u− uk‖2

=

[(β +

1

λk

)I + γB∗A−∗A−1B

]−1( 1

λkuk + γB∗A−∗z

).

• Proximal-point step:

uk+1 = arg minu∈L2

αc‖u‖1,T +

1

2λk‖u− uk‖2T

= shrink(uk, αcλk).

Remark 2.1 Notice that, when αs = 0, the solution of (Pcs) is simply given by

u = γ[βI + γB∗A−∗A−1B

]−1B∗A−∗z.

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2.2. Evolutionary problem

(Pc) u ∈ arg minu∈L2

T

J (u) = αc‖u‖1,T +

β

2‖u‖2T +

γ

2‖Lu− z‖2T

.

2.2.1. Optimality conditions

Define the classical Lagrangian

L (u, y, p) = J (u) + 〈p,Bu− y′ −Ay〉T .

By integration by parts, we have

L (u, y, p) = αc‖u‖1,T +β

2‖u‖2T +

γ

2‖y − z‖2T + 〈B∗p, u〉T

+ 〈p′ −A∗p, y〉T + 〈p(0), y(0)〉 − 〈p(T ), y(T )〉.

Deriving with respect to the three variables (u, y, p), we obtain the optimalitysystem:

y′ +Ay = Bu (Ω× (0, T ))

−p′ +A∗p = γ (y − z) (Ω× (0, T ))

y = 0, p = 0 (∂Ω× (0, T ))

y(0) = 0, p(T ) = 0 (Ω) ,

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

0 ∈ αc ∂‖ · ‖1,T (u) + βu+B∗p.

The latter is equivalent to

u = (βI + αc ∂‖ · ‖1,T )−1 (−B∗p)

= arg minv∈L2

T

αc‖v‖1,T +

1

2β‖v +B∗p‖2T

= shrink(−B∗p, αc

β),

where the operator of soft− shrinkage is defined by

shrink(t, α) =

t+ α (t < −α)

0 (−α ≤ t ≤ α)

t− α (t > α).

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Finally, y′ +Ay = B shrink(−B∗p, αc

β ) (Ω× (0, T ))

−p′ +A∗p = γ (y − z) (Ω× (0, T ))

y = 0, p = 0 (∂Ω× (0, T ))

y(0) = 0, p(T ) = 0 (Ω) .

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2.2.2. Numerical algorithm

In order to compute a numerical solution of problem (Pc), after a discretization byfinite differences, we use a grad-prox splitting:

• Gradient step:

uk = uk − λk∇u[β

2‖u‖2T +

γ

2‖Lu− z‖2T

](uk)

= uk − λk [βuk + γL∗ (Luk − z)]

= uk − λk [βuk + γB∗pk] ,

where y′k +Ayk = Buk (Ω× (0, T ))

yk = 0 (∂Ω× (0, T ))

yk(0) = 0 (Ω)

and −p′k +A∗pk = yk − z (Ω× (0, T ))

pk = 0 (∂Ω× (0, T ))

pk(T ) = 0 (Ω) .

• Proximal-point step:

uk+1 = arg minu∈L2

T

αc‖u‖1,T +

1

2λk‖u− uk‖2T

= shrink(uk, αcλk).

Remark 2.2 Another possibility is to include the term β2 ‖u‖

2T in the proximal step.

Remark 2.3 Notice that, for

f(u) =β

2‖u‖2T +

γ

2‖Lu− z‖2T ,

then ∇f is Lipschitz continuous. Indeed, for ui ∈ L2T (i = 1, 2), then

∇f(ui) = βui + γB∗pi,

where y′i +Ayi = Bui (Ω× (0, T ))

yi = 0 (∂Ω× (0, T ))

yi(0) = 0 (Ω)

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and −p′i +A∗pi = yi − z (Ω× (0, T ))

pi = 0 (∂Ω× (0, T ))

pi(T ) = 0 (Ω) .

By linearity δy = y2 − y1 and δp = p2 − p1 solve the same equations with right-hand-sides B(u2 − u1) and δy, respectively. Then

‖∇f(u2)−∇f(u1)‖ ≤ β‖u2 − u1‖T + γ‖B∗‖‖δp‖T≤ β‖u2 − u1‖T + γ Cadj‖B‖‖δy‖T≤ β‖u2 − u1‖T + γ CadjC ‖B‖‖B(u2 − u1)‖T≤ L‖u2 − u1‖T ,

where we defined

L = β + γ CadjC ‖B‖2.

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

0 < λ ≤ λk ≤ Λ <2

L.

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3. Sparse state: αs > 0 (αc = 0)

(Ps) u ∈ arg minu∈L2

T

J (u) =

β

2‖u‖2T + αs‖Lu‖1,T +

γ

2‖Lu− z‖2T

.

3.1. The stationary problem

(SPs) u ∈ arg minu∈L2

Js (u) = αc‖u‖1 +

β

2‖u‖2 +

γ

2‖y − z‖2 : Ay = Bu

.

3.1.1. Optimality conditions

Ay = − 1

βBB∗p (Ω× (0, T ))

y = shrink(A∗p+ γz, αs

γ ) (Ω× (0, T ))

y = 0, p = 0 (∂Ω× (0, T )) .

Finally, we obtain a single equation in the dual variable p:A shrink(A∗p+ γz, αs

γ ) = − 1βBB

∗p (Ω× (0, T ))

p = 0 (∂Ω× (0, T )) .

3.1.2. Numerical algorithm

In order to compute a numerical solution of problem (Ps), after a discretizationby finite differences, we use a prox-prox splitting on the Augmented Energy: firstwrite the state as y = A−1Bu, then

• Proximal-point step:

uk+1 = arg minu∈L2

β

2‖u‖2 +

γ

2‖A−1Bu− z‖2 +

δ

2λk‖A−1Bu− yk‖2 +

1

2λk‖u− uk‖2

=

[(β +

1

λk

)I +

(γ +

δ

λk

)B∗A−∗A−1B

]−1 [ 1

λkuk +B∗A−∗

(γz +

δ

λkyk

)].

• Proximal-point step:

yk+1 = arg miny∈L2

αs‖y‖1 +

δ

2λk‖y −A−1Buk+1‖2 +

1

2λk‖y − yk‖2T

= shrink(yk, λk),

where we defined

yk =yk + δA−1Buk+1

1 + δ;

λk =αsλk1 + δ

.

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Remark 3.1 Notice that again, when αs = 0, the solution of (Ps) is simply givenby

u = γ[βI + γB∗A−∗A−1B

]−1B∗A−∗z.

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3.2. Evolutionary problem

3.2.1. Optimality conditions

Define the classical Lagrangian

L (u, y, p) = J (u) + 〈p,Bu− y′ −Ay〉T .

By integration by parts, we have

L (u, y, p) =β

2‖u‖2T + αs‖y‖1,T +

γ

2‖y − z‖2T + 〈B∗p, u〉T

+ 〈p′ −A∗p, y〉T + 〈p(0), y(0)〉 − 〈p(T ), y(T )〉.

Deriving with respect to the three variables (u, y, p), we obtain the optimalitysystem:

y′ +Ay = Bu (Ω× (0, T ))

−p′ +A∗p ∈ γ (y − z) + αs ∂‖ · ‖1,T (y) (Ω× (0, T ))

y = 0, p = 0 (∂Ω× (0, T ))

y(0) = 0, p(T ) = 0 (Ω) ,

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

u = − 1

βB∗p.

The adjoint equation is equivalent to

y = (γI + αs ∂‖ · ‖1,T )−1 (−p′ +A∗p+ γz)

= shrink(−p′ +A∗p+ γz,αsγ

).

Finally, y′ +Ay = − 1

βBB∗p (Ω× (0, T ))

y = shrink(−p′ +A∗p+ γz, αs

γ ) (Ω× (0, T ))

y = 0, p = 0 (∂Ω× (0, T ))

y(0) = 0, p(T ) = 0 (Ω) ,

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3.2.2. Numerical algorithm

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

Lλ (u, y) =β

2‖u‖2T + αs‖y‖1,T +

γ

2‖Lu− z‖2T +

δ

2λ‖Lu− y‖2T .

Then,

• Gradient step:

uk+1 = uk − λk∇u[β

2‖u‖2T +

γ

2‖Lu− z‖2T +

δ

2λ‖Lu− y‖2T

](uk)

= uk − λk[βuk + γL∗ (Luk − z) +

δ

λkL∗ (Luk − yk)

]= (1− βλk)uk −B∗pk,

where y′uk

+Ayuk= Buk (Ω× (0, T ))

yuk= 0 (∂Ω× (0, T ))

yuk(0) = 0 (Ω)

and −p′k +A∗pk = (γλk + δ) yuk

− γλkz − δyk (Ω× (0, T ))

pk = 0 (∂Ω× (0, T ))

pk(T ) = 0 (Ω) .

• Proximal-point step:

yk+1 = arg miny∈L2

T

αs‖y‖1,T +

δ

2λk‖y − Luk+1‖2T +

1

2λk‖y − yk‖2T

= shrink(yk, λk),

where we defined

yk =yk + δLuk+1

1 + δ;

λk =αsλk1 + δ

.

Remark 3.2 Another possibility is to consider

Lλ (u, y) =β

2‖u‖2T + αs‖y‖1,T +

γ

2‖y − z‖2T +

δ

2λ‖Lu− y‖2T .

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4. Computational experiments

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

• Spacial domain: Ω = (0, 1);• Time interval: [0, T ], with T = 1;• Weight-parameters: αc = [0, 0.01], αs = [0, 0.65], β = 0.0001 and γ = 1;• Trajectory target:

z(x) = I[xa,xb],

where xa = 1.7/3, xb = 3.5/4;• Control operator: for x1 = 1/7 and x2 = 4/5,

B = I[x1,x2];

• A is the finite difference discretization of −∆;• Numerical grid: Nx = 300 in space, Nt = 100 in time.

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4.1. Stationary solutions

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Figure 1.: αc = αs = 0.

Figure 2.: αc = 0.01, αs = 0.

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Figure 3.: αc = 0, αs = 0.65.

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4.2. Evolutionary problem

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Figure 4.: Optimal control for αc = αs = 0 (TOP), αc = 0.01, αs = 0 (MIDDLE)and αc = 0, αs = 0.65 (BOTTOM). In red, the controllable subdomain; in blue,the stationary optimal controls.

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Figure 5.: Optimal state for αc = αs = 0 (TOP), αc = 0.01, αs = 0 (MIDDLE)and αc = 0, αs = 0.65 (BOTTOM). In red, the target z; in blue, the stationaryoptimal states.

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Figure 6.: Optimal adjoint for αc = αs = 0 (TOP), αc = 0.01, αs = 0 (MIDDLE)and αc = 0, αs = 0.65 (BOTTOM).

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References

[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.

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