High dimensional sparse polynomial approximations of ... · The curse of dimensionality Consider a...

High dimensional sparse polynomial approximationsof parametric and stochastic PDE’s

Albert Cohen

Laboratoire Jacques-Louis LionsUniversite Pierre et Marie Curie

with Ronald DeVore and Christoph Schwabnumerical results by Abdellah Chkifa

Banff, 2011

The curse of dimensionality

Consider a continuous function y 7→ u(y) with y ∈ [0,1]. Sample at equispaced points.Reconstruct, for example by piecewise linear interpolation.

Error in terms of point spacing h > 0 : if u has C2 smoothness

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

Using piecewise polynomials of higher order, if u has Cm smoothness

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

In terms of the number of samples N ∼ h−1, the error is estimated by N−m.

In d dimensions : u(y) = u(y1, · · · ,yd) with y ∈ [0,1]d . With a uniform sampling, westill have

‖u −R(u)‖L∞ ≤ C‖dmu‖L∞ hm,

but the number of samples is now N ∼ h−d , and the error estimate is in N−m/d .

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

‖u −R(u)‖L∞ ≤ C‖u ′′‖L∞ h2.

‖u −R(u)‖L∞ ≤ C‖u(m)‖L∞ hm.

Other sampling/reconstruction methods cannot do better !

Can be explained by nonlinear manifold width (DeVore-Howard-Micchelli).

Let X be a normed space and K ⊂ X a compact set.

Consider maps E : K 7→ RN (encoding) and R : R

N 7→ X (reconstruction).

Introducing the distorsion of the pair (E ,R) over K

maxu∈K

‖u −R(E(u))‖X ,

we define the nonlinear N-width of K as

dN(K) := infE ,R

maxu∈K

‖u −R(E(u))‖X ,

where the infimum is taken over all continuous maps (E ,R).

If X = L∞ and K is the unit ball of Cm([0,1]d ) it is known that

cN−m/d ≤ dN(K) ≤ CN−m/d .

maxu∈K

‖u −R(E(u))‖X ,

dN(K) := infE ,R

maxu∈K

‖u −R(E(u))‖X ,

maxu∈K

‖u −R(E(u))‖X ,

dN(K) := infE ,R

maxu∈K

‖u −R(E(u))‖X ,

maxu∈K

‖u −R(E(u))‖X ,

dN(K) := infE ,R

maxu∈K

‖u −R(E(u))‖X ,

maxu∈K

‖u −R(E(u))‖X ,

dN(K) := infE ,R

maxu∈K

‖u −R(E(u))‖X ,

High dimensional problems occur frequently

PDE’s with solutions u(x,v,t) defined in phase space : d = 7.

Post-processing of numerical codes : u solver with imput parameters (y1, · · · ,yd).

Learning theory : u regression function of imput parameters (y1, · · · ,yd)

In these applications d may be of the order up to 103.

Approximation of stochastic-parametric PDEs (this talk) : d = +∞.

Smoothness properties of functions should be revisited by other means than Cm

classes, and appropriate approximation tools should be used.

Key ingredients :

(i) Sparsity

(ii) Variable reduction

(iii) Anisotropy

Key ingredients :

(i) Sparsity

(iii) Anisotropy

Key ingredients :

(i) Sparsity

(iii) Anisotropy

Key ingredients :

(i) Sparsity

(iii) Anisotropy

A model elliptic PDE

We consider the steady state diffusion equation

−div(a∇u) = f in D ⊂ IRm and u = 0 on ∂D,

where f = f (x) ∈ L2(D) and a = a(x,y) are variable coefficients depending on x ∈ D

and on a vector y of parameters in an affine manner :

a = a(x,y) = a(x)+∑

yjψj(x), x ∈ D,y = (yj)j>0 ∈ U := [−1,1]N,

where (ψj)j>0 is a given family of functions.

The parameters may be deterministic (control, optimization) or random (uncertaintymodeling and propagation, reliability assessment).

Uniform ellipticity assumption :

(UEA) 0 < r ≤ a(x,y) ≤ R, x ∈ D, y ∈ U.

Then u : y 7→ u(y) = u(·,y) is a bounded map from U to V := H10 (Ω) :

‖u(y)‖V ≤ C0 :=‖f ‖V∗

r, y ∈ U, where ‖v‖V := ‖∇v‖L2 .

Proof : multiply equation by u and integrate

r‖u‖2V ≤

a∇u · ∇u = −

u div(a∇u) =

uf ≤ ‖u‖V ‖f ‖V∗ .

Objective : build a computable approximation to this map at reasonable cost, i.e.simultaneaously approximate u(y) for all y ∈ U.

a = a(x,y) = a(x)+∑

yjψj(x), x ∈ D,y = (yj)j>0 ∈ U := [−1,1]N,

(UEA) 0 < r ≤ a(x,y) ≤ R, x ∈ D, y ∈ U.

‖u(y)‖V ≤ C0 :=‖f ‖V∗

r, y ∈ U, where ‖v‖V := ‖∇v‖L2 .

r‖u‖2V ≤

a∇u · ∇u = −

u div(a∇u) =

uf ≤ ‖u‖V ‖f ‖V∗ .

a = a(x,y) = a(x)+∑

yjψj(x), x ∈ D,y = (yj)j>0 ∈ U := [−1,1]N,

(UEA) 0 < r ≤ a(x,y) ≤ R, x ∈ D, y ∈ U.

‖u(y)‖V ≤ C0 :=‖f ‖V∗

r, y ∈ U, where ‖v‖V := ‖∇v‖L2 .

r‖u‖2V ≤

a∇u · ∇u = −

u div(a∇u) =

uf ≤ ‖u‖V ‖f ‖V∗ .

a = a(x,y) = a(x)+∑

yjψj(x), x ∈ D,y = (yj)j>0 ∈ U := [−1,1]N,

(UEA) 0 < r ≤ a(x,y) ≤ R, x ∈ D, y ∈ U.

‖u(y)‖V ≤ C0 :=‖f ‖V∗

r, y ∈ U, where ‖v‖V := ‖∇v‖L2 .

r‖u‖2V ≤

a∇u · ∇u = −

u div(a∇u) =

uf ≤ ‖u‖V ‖f ‖V∗ .

a = a(x,y) = a(x)+∑

yjψj(x), x ∈ D,y = (yj)j>0 ∈ U := [−1,1]N,

(UEA) 0 < r ≤ a(x,y) ≤ R, x ∈ D, y ∈ U.

‖u(y)‖V ≤ C0 :=‖f ‖V∗

r, y ∈ U, where ‖v‖V := ‖∇v‖L2 .

r‖u‖2V ≤

a∇u · ∇u = −

u div(a∇u) =

uf ≤ ‖u‖V ‖f ‖V∗ .

Polynomial expansions

Use of multivariate polynomials in the y variable.

Sometimes referred to as “polynomial chaos” in the random setting (Ghanem-Spanos,Babushka-Tempone-Nobile-Zouharis, Karniadakis, Schwab...).

We study the convergence of the Taylor development

u(y) =∑

ν∈F

tνyν,

whereyν :=

Here F is the set of all finitely supported sequences ν= (νj)j>0 of integers (onlyfinitely many νj are non-zero). The Taylor coefficients tν ∈ V are

tν :=1

ν!∂νu|y=0 with ν! :=

νj ! and 0! := 1.

We also studied Legendre series u(y) =∑ν∈F uνLν where Lν(y) :=

∏j>0 Lνj (yj).

Polynomial expansions

Use of multivariate polynomials in the y variable.

Sometimes referred to as “polynomial chaos” in the random setting (Ghanem-Spanos,Babushka-Tempone-Nobile-Zouharis, Karniadakis, Schwab...).

We study the convergence of the Taylor development

u(y) =∑

ν∈F

tνyν,

whereyν :=

Here F is the set of all finitely supported sequences ν= (νj)j>0 of integers (onlyfinitely many νj are non-zero). The Taylor coefficients tν ∈ V are

tν :=1

ν!∂νu|y=0 with ν! :=

νj ! and 0! := 1.

We also studied Legendre series u(y) =∑ν∈F uνLν where Lν(y) :=

∏j>0 Lνj (yj).

Sparse N-term polynomial approximation

The sequence (tν)ν∈F is indexed by countably many integers.

νObjective : identify a set Λ⊂ F with #(Λ) ≤ N such that u is well approximated inthe space

VΛ := ∑

ν∈Λ

cνyν ; uν ∈ V ,

for example by the partial Taylor expansion

uΛ(y) :=∑

ν∈Λ

tνyν.

Sparse N-term polynomial approximation

The sequence (tν)ν∈F is indexed by countably many integers.

νObjective : identify a set Λ⊂ F with #(Λ) ≤ N such that u is well approximated inthe space

VΛ := ∑

ν∈Λ

cνyν ; uν ∈ V ,

for example by the partial Taylor expansion

uΛ(y) :=∑

ν∈Λ

tνyν.

Best N-term approximation

A-priori choices for Λ have been proposed : (anisotropic) sparse grid defined byrestrictions of the type

∑j αjνj ≤ A(N) or

∏j(1 +βjνj) ≤ B(N).

Instead we want study a choice of Λ optimally adapted to u.

For all y ∈ U = [−1,1]N we have

‖u(y)−uΛ(y)‖V ≤ ‖∑

ν/∈Λ

tνyν‖V ≤∑

ν/∈Λ

‖tν‖V

Best N-term approximation in the ℓ1(F) norm : use for Λ the N largest ‖tν‖V .

Observation (Stechkin) : if (‖tν‖V )ν∈F ∈ ℓp(F) for some p < 1, then for this Λ,

ν/∈Λ

‖tν‖V ≤ CN−s , s :=1

p−1, C := ‖(‖tν‖V )‖p.

Proof : with (tn)n>0 the decreasing rearrangement, we combine

ν/∈Λ

‖tν‖V =∑

tn =∑

t1−pn tp

n ≤ t1−pN

Cp and NtpN≤

tpn ≤ Cp.

Question : do we have (‖tν‖V )ν∈F ∈ ℓp(F) for some p < 1 ?

∏j(1 +βjνj) ≤ B(N).

‖u(y)−uΛ(y)‖V ≤ ‖∑

ν/∈Λ

tνyν‖V ≤∑

ν/∈Λ

‖tν‖V

ν/∈Λ

‖tν‖V ≤ CN−s , s :=1

p−1, C := ‖(‖tν‖V )‖p.

ν/∈Λ

‖tν‖V =∑

tn =∑

t1−pn tp

n ≤ t1−pN

Cp and NtpN≤

tpn ≤ Cp.

∏j(1 +βjνj) ≤ B(N).

‖u(y)−uΛ(y)‖V ≤ ‖∑

ν/∈Λ

tνyν‖V ≤∑

ν/∈Λ

‖tν‖V

ν/∈Λ

‖tν‖V ≤ CN−s , s :=1

p−1, C := ‖(‖tν‖V )‖p.

ν/∈Λ

‖tν‖V =∑

tn =∑

t1−pn tp

n ≤ t1−pN

Cp and NtpN≤

tpn ≤ Cp.

∏j(1 +βjνj) ≤ B(N).

‖u(y)−uΛ(y)‖V ≤ ‖∑

ν/∈Λ

tνyν‖V ≤∑

ν/∈Λ

‖tν‖V

ν/∈Λ

‖tν‖V ≤ CN−s , s :=1

p−1, C := ‖(‖tν‖V )‖p.

ν/∈Λ

‖tν‖V =∑

tn =∑

t1−pn tp

n ≤ t1−pN

Cp and NtpN≤

tpn ≤ Cp.

The main result

Theorem (Cohen-DeVore-Schwab, 2009) : under the uniform ellipticity assumption(UAE), then for any p < 1,

(‖ψj‖L∞ )j≥0 ∈ ℓp(N) ⇒ (‖tν‖V )ν∈F ∈ ℓp(F).

Interpretations :

(i) The Taylor expansion of u(y) inherits the sparsity properties of the expansion ofa(y) into the ψj .

(ii) We approximate u(y) in L∞ (U) with algebraic rate N−s despite the curse of(infinite) dimensionality, due to the fact that yj is less influencial as j gets large.

(iii) The set K := u(y) ; y ∈ U is compact in V and has small N-widthdN(K) := infdim(E)≤N maxv∈K dist(v,E)V : for all y

uΛ(y) :=∑

ν∈Λ

tνyν =∑

ν∈Λ

yνtν ∈ EΛ := Spantν ; ν∈ Λ.

With Λ corresponding to the N largest ‖tν‖V , we find that

dN(K) ≤ maxy∈U

dist(u(y),EΛ)V ≤ maxy∈U

‖u(y)−uΛ(y)‖V ≤ CN−s .

Such approximation rates cannot be proved for the usual a-priori choices of Λ.

The main result

Interpretations :

uΛ(y) :=∑

ν∈Λ

tνyν =∑

ν∈Λ

dN(K) ≤ maxy∈U

The main result

Interpretations :

uΛ(y) :=∑

ν∈Λ

tνyν =∑

ν∈Λ

dN(K) ≤ maxy∈U

The main result

Interpretations :

uΛ(y) :=∑

ν∈Λ

tνyν =∑

ν∈Λ

dN(K) ≤ maxy∈U

Idea of proof : extension to complex variable

Estimates on ‖tν‖V by complex analysis : extend u(y) to u(z) with z = (zj) ∈ C|| IN.

Uniform ellipticity 0 < r ≤ a(x)+∑

j>0 yjψj(x) for all x ∈ D,y ∈ U is equivalent to

|ψj(x)| ≤ a(x)− r, x ∈ D.

This allows to say that with a(x,z) = a(x)+∑

j>0 zjψj(x),

0 < r ≤ ℜ(a(x,z)) ≤ |a(x,z)| ≤ 2R,

for all z ∈ U := |z | ≤ 1N =⊗|zj | ≤ 1.

Lax-Milgram theory applies : ‖u(z)‖ ≤ C0 =‖f ‖V∗

rfor all z ∈ U . The function

u 7→ u(z) is holomorphic in each variable zj at any z ∈ U .

Extended domains of holomorphy : if ρ= (ρj)j≥0 is any positive sequence such thatfor some δ> 0 ∑

ρj |ψj(x)| ≤ a(x)−δ, x ∈ D,

then u is holomorphic with uniform bound ‖u(z)‖ ≤ Cδ =‖f ‖V∗

δin the polydisc

Uρ :=⊗|zj | ≤ ρj ,

We call such sequences ρ “δ-admissible”. If δ< r , we can take ρj > 1.

|ψj(x)| ≤ a(x)− r, x ∈ D.

j>0 zjψj(x),

0 < r ≤ ℜ(a(x,z)) ≤ |a(x,z)| ≤ 2R,

for all z ∈ U := |z | ≤ 1N =⊗|zj | ≤ 1.

ρj |ψj(x)| ≤ a(x)−δ, x ∈ D,

δin the polydisc

Uρ :=⊗|zj | ≤ ρj ,

|ψj(x)| ≤ a(x)− r, x ∈ D.

j>0 zjψj(x),

0 < r ≤ ℜ(a(x,z)) ≤ |a(x,z)| ≤ 2R,

for all z ∈ U := |z | ≤ 1N =⊗|zj | ≤ 1.

ρj |ψj(x)| ≤ a(x)−δ, x ∈ D,

δin the polydisc

Uρ :=⊗|zj | ≤ ρj ,

|ψj(x)| ≤ a(x)− r, x ∈ D.

j>0 zjψj(x),

0 < r ≤ ℜ(a(x,z)) ≤ |a(x,z)| ≤ 2R,

for all z ∈ U := |z | ≤ 1N =⊗|zj | ≤ 1.

ρj |ψj(x)| ≤ a(x)−δ, x ∈ D,

δin the polydisc

Uρ :=⊗|zj | ≤ ρj ,

Estimate on the Taylor coefficients

Use Cauchy formula. In 1 complex variable if z 7→ u(z) is holomorphic and bounded ina neighbourhood of disc |z | ≤ a, then for all z in this disc

u(z) =1

|z ′|=a

u(z ′)

z − z ′dz ′,

which leads by m differentiation at z = 0 to |u(m)(0)| ≤ m!a−m max|z|≤a |u(z)|.

Recursive application of this to all variables zj such that νj 6= 0, with a = ρj , for aδ-admissible sequence ρ gives

‖∂νu|z=0‖V ≤ Cδν!∏

ρ−νj

and therefore‖tν‖V ≤ Cδ

ρ−νj

j= C0ρ

−ν.

Since ρ is not fixed we have

‖tν‖V ≤ Cδ infρ−ν ; ρ is δ−admissible.

We do not know the general solution to this problem, except when the ψj havedisjoint supports. Instead design a particular choice ρ=ρ(ν) of δ-admissiblesequences with δ= r/2, for which we prove that

(‖ψj‖L∞ )j≥0 ∈ ℓp(N) ⇒ (ρ(ν)−ν)ν∈F ∈ ℓp(F).

u(z) =1

|z ′|=a

u(z ′)

z − z ′dz ′,

‖∂νu|z=0‖V ≤ Cδν!∏

ρ−νj

j= C0ρ

−ν.

u(z) =1

|z ′|=a

u(z ′)

z − z ′dz ′,

‖∂νu|z=0‖V ≤ Cδν!∏

ρ−νj

j= C0ρ

−ν.

A simple case

Assume that the ψj have disjoint supports. Then we maximize separately the ρj sothat ∑

ρj |ψj (x)| ≤ a(x)−r

2, x ∈ D,

which leads to

ρj := minx∈D

a(x)− r2

|ψj(x)|.

We have‖tν‖V ≤ 2C0ρ

−ν = 2C0bν,

where b = (bj) and

bj := ρ−1j

=|ψj(x)|

a(x)− r2

≤‖ψj‖L∞

R − r2

Therefore b ∈ ℓp(N). From (UEA), we have |ψj (x)| ≤ a(x)− r and thus ‖b‖ℓ∞ < 1.

We finally observe that

b ∈ ℓp(N) and ‖b‖ℓ∞ < 1 ⇔ (bν)ν∈F ∈ ℓp(F).

Proof : factorize ∑

ν∈F

bpν =∏

1 −bpj

A simple case

2, x ∈ D,

which leads to

ρj := minx∈D

a(x)− r2

|ψj(x)|.

−ν = 2C0bν,

where b = (bj) and

bj := ρ−1j

=|ψj(x)|

a(x)− r2

≤‖ψj‖L∞

R − r2

ν∈F

bpν =∏

1 −bpj

A simple case

2, x ∈ D,

which leads to

ρj := minx∈D

a(x)− r2

|ψj(x)|.

−ν = 2C0bν,

where b = (bj) and

bj := ρ−1j

=|ψj(x)|

a(x)− r2

≤‖ψj‖L∞

R − r2

ν∈F

bpν =∏

1 −bpj

An adaptive algorithm

Strategies to build the set Λ :

(i) Non-adaptive, based on the available a-priori estimates for the ‖tν‖V .

(ii) Adaptive, based on a-posteriori information gained in the computationΛ1 ⊂ Λ2 ⊂ · · · ⊂ ΛN .

Objective : develop adaptive strategies that converge with optimal rate (similar toadaptive wavelet methods for elliptic PDE’s : Cohen-Dahmen-DeVore, Stevenson).

Recursive computation of the Taylor coefficients : with ej the Kroenecker sequence

a∇tν∇v = −∑

j : νj 6=0

ψj∇tν−ej∇v, v ∈ V .

We compute the tν on sets Λ with monotone structure : ν∈ Λ and µ≤ ν⇒ µ∈ Λ.

Given such a Λk and the (tν)ν∈Λkwe compute the tν for ν in the margin

Mk := ν /∈ Λk ; ν− ej ∈ Λk for some j,

and build the new set by bulk search : Λk+1 =Λk ∪ Sk , with Sk ⊂ Mk smallest suchthat

∑ν∈Sk

‖tν‖2V

≥ θ∑ν∈Mk

‖tν‖2V

, with θ∈ (0,1).

Such a strategy can be proved to converge with optimal convergence rate #(Λk)−s .

a∇tν∇v = −∑

j : νj 6=0

∑ν∈Sk

‖tν‖2V

≥ θ∑ν∈Mk

‖tν‖2V

, with θ∈ (0,1).

a∇tν∇v = −∑

j : νj 6=0

∑ν∈Sk

‖tν‖2V

≥ θ∑ν∈Mk

‖tν‖2V

, with θ∈ (0,1).

a∇tν∇v = −∑

j : νj 6=0

∑ν∈Sk

‖tν‖2V

≥ θ∑ν∈Mk

‖tν‖2V

, with θ∈ (0,1).

a∇tν∇v = −∑

j : νj 6=0

∑ν∈Sk

‖tν‖2V

≥ θ∑ν∈Mk

‖tν‖2V

, with θ∈ (0,1).

Test case in moderate dimension d = 16

Physical domain D = [0,1]2 =∪dj=1Dj .

Diffusion coefficients a(x,y) = 1 +∑d

j=1 yj

Adaptive search of Λ implemented in C++, spatial discretization by FreeFem++.

Comparison between the Λk generated by the adaptive algorithm (red) andnon-adaptive choices supνj ≤ k (blue) or

∑νj ≤ k (green) or k largest a-priori

bounds on the ‖tν‖V (pink)

10−10

10−9

10−8

10−7

10−6

10−5

10−4

10−3

10−2

algo BSalgo Pkalgo Qkalgo MEalgo NN

Highest polynomial degree with #(Λ) = 1000 coefficients : 1, 4, 115 and 81.

What I did not speak about

Use of Legendre polynomials instead of Taylor series : leads to approximation errorestimates in L2(U,dµ) with dµ the tensor product probability measure on U.

Computation of the approximate Legendre coefficients : either use a Galerkin(projection) method or a Collocation (interpolation) method. For the second one,designing optimal collocation points is an open problem.

(i) A-priori, based on the available estimates for the ‖tν‖V .

(ii) A-posteriori, based on error indicators in the Galerkin framework :Λ1 ⊂ Λ2 ⊂ · · · ⊂ ΛN . Optimal convergence of this strategy may be proved by similartechniques as for adaptive wavelet methods for elliptic PDE’s(Cohen-Dahmen-DeVore, Stevenson).

(iii) Reconstruction a sparse orthogonal series from random sampling : techniquesfrom Compressed Sensing (Sparse Fourier series : Gilbert-Strauss-Tropp,Candes-Romberg-Tao, Rudelson-Vershynin. Sparse Legendre series : Rauhut-Ward2010).

Space discretization : should be properly tuned (use different resolution for each tν)and injected in the final error analysis.

Our results can be used in the analysis of reduced basis methods.

Conclusion and perspective

Rich topic : involves a variety of tools such as stochastic processes, high dimensionalapproximation, complex analysis, sparsity and non-linear approximation, adaptivity anda-posteriori analysis.

First numerical results in moderate dimensionality : reveal the adavantages of anadaptive approach. Goal : implementation for very high or infinite dimensionality.

Many applications in engineering.

Many other models to be studied :

(i) Non-affine dependence of a in the variable y .

(ii) Other linear or non-linear PDE’s.

Papers : www.ann.jussieu.fr/cohen

THANKS !

High dimensional sparse polynomial approximations of ... · The curse of dimensionality Consider a...

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