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TRANSCRIPT
Gelfand Widths in Compressive Sensing
Optimal Number of Measurements
Björn Bringmann
Technische Universität München
15.01.2015
Björn Bringmann Gelfand Widths in Compressive Sensing 1 / 26
Outline
1 Introduction to m-Widths
Kolmogorov and Gelfand Widths
Compressive m-widths
2 Gelfand Widths of l1-balls
Upper bound
Lower bound
3 Applications
Optimal Number of Measurements
Kashin’s Decomposition Theorem
Björn Bringmann Gelfand Widths in Compressive Sensing 2 / 26
Introduction to m-Widths
Outline
1 Introduction to m-Widths
Kolmogorov and Gelfand Widths
Compressive m-widths
2 Gelfand Widths of l1-balls
Upper bound
Lower bound
3 Applications
Optimal Number of Measurements
Kashin’s Decomposition Theorem
Björn Bringmann Gelfand Widths in Compressive Sensing 3 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Motivation
LetX = {f ∈ C(R) : f is 2π periodic}
and define
Sn(f )(x) :=n
∑k=−n
f̂ (k)eikx .
How fast does ‖f −Sn(f )‖2 converge to 0?
Approximate for f ∈ C([a,b])∫ b
af (x)dx ≈
n
∑k=0
wk f (xk ) .
What is a reasonable error estimate?
Conclusion: Approximation by linear subspaces is a good idea!
Björn Bringmann Gelfand Widths in Compressive Sensing 4 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Motivation
LetX = {f ∈ C(R) : f is 2π periodic}
and define
Sn(f )(x) :=n
∑k=−n
f̂ (k)eikx .
How fast does ‖f −Sn(f )‖2 converge to 0?
Approximate for f ∈ C([a,b])∫ b
af (x)dx ≈
n
∑k=0
wk f (xk ) .
What is a reasonable error estimate?
Conclusion: Approximation by linear subspaces is a good idea!
Björn Bringmann Gelfand Widths in Compressive Sensing 4 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Motivation
LetX = {f ∈ C(R) : f is 2π periodic}
and define
Sn(f )(x) :=n
∑k=−n
f̂ (k)eikx .
How fast does ‖f −Sn(f )‖2 converge to 0?
Approximate for f ∈ C([a,b])∫ b
af (x)dx ≈
n
∑k=0
wk f (xk ) .
What is a reasonable error estimate?
Conclusion: Approximation by linear subspaces is a good idea!
Björn Bringmann Gelfand Widths in Compressive Sensing 4 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Motivation
LetX = {f ∈ C(R) : f is 2π periodic}
and define
Sn(f )(x) :=n
∑k=−n
f̂ (k)eikx .
How fast does ‖f −Sn(f )‖2 converge to 0?
Approximate for f ∈ C([a,b])∫ b
af (x)dx ≈
n
∑k=0
wk f (xk ) .
What is a reasonable error estimate?
Conclusion: Approximation by linear subspaces is a good idea!
Björn Bringmann Gelfand Widths in Compressive Sensing 4 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Kolmogorov m-WidthDefinitionLet X be a real (or complex) Banach space. For any subset K ⊂ X andany m-dimensional linear subspace Xm ⊂ X define
d(K ,Xm;X ) := supx∈K
infz∈Xm‖x−z‖X .
Now define the Kolmogorov m-width as
dm(K ;X ) := inf{d(K ,Xm;X ) : Xm ⊂ X m-dimensional linear subspace} .
x
y
KX1
Björn Bringmann Gelfand Widths in Compressive Sensing 5 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Kolmogorov m-WidthDefinitionLet X be a real (or complex) Banach space. For any subset K ⊂ X andany m-dimensional linear subspace Xm ⊂ X define
d(K ,Xm;X ) := supx∈K
infz∈Xm‖x−z‖X .
Now define the Kolmogorov m-width as
dm(K ;X ) := inf{d(K ,Xm;X ) : Xm ⊂ X m-dimensional linear subspace} .
x
y
KX1
Björn Bringmann Gelfand Widths in Compressive Sensing 5 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Properties of the Kolmogorov m-width
LemmaLet X be a Banach space and K ⊂ X,m ∈ N.
1 dm(K ;X ) = dm(K̄ ;X ), where K̄ is the closure of K .
2 For every scalar α there holds dm(αK ;X ) = |α|dm(K ;X ).
3 dm(co(K );X ) = dm(K ;X ), where co(K ) is the convex hull of K .
4 dm(K ;X )≥ dm+1(K ;X ).
Björn Bringmann Gelfand Widths in Compressive Sensing 6 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Gelfand m-Width
DefinitionLet X be a real or complex Banach space and K a subset of X. TheGelfand m-width is defined as
dm(K ;X ) = inf{ supx∈K∩Lm
‖x‖X : Lm⊂X linear subspace with codim(Lm)≤m}
We say that a closed linear subspace Lm ⊂ X has codim(Lm) = m ifdim(X/Lm) = m .
Lemma
A linear subspace Lm ⊂ X has codim(Lm)≤m if and only if there existf1, . . . , fm ∈ X ∗ such that
Lm = {x ∈ X : fi(x) = 0 ∀i = 1, . . . ,m} .
Björn Bringmann Gelfand Widths in Compressive Sensing 7 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Duality of Kolmogorov and Gelfand m-widths
Theorem
For 1≤ p,q ≤ ∞ let p∗,q∗ be such that 1p + 1
p∗ = 1 and 1q + 1
q∗ = 1. Then
dm(BNp ;`N
q ) = dm(BNq∗ ;`
Np∗) .
LemmaLet Y be a finite-dimensional subspace of a Banach space X. Givenx ∈ X\Y and y∗ ∈ Y, the following properties are equivalent:
1 y∗ is a best approximation to x from Y .2 For some λ ∈ X ∗ with ‖λ‖X ∗ ≤ 1 and λ |Y ≡ 0, there holds
‖x−y∗‖= λ (x) .
Björn Bringmann Gelfand Widths in Compressive Sensing 8 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Connection with linear operators
Note that
dm(K ;X ) = inf
{sup
x∈K∩Ker(A)‖x‖ : A : X →Km linear, continuous
}.
If Lm is a subspace with codim(Lm)≤m, then choose f1, . . . , fm ∈ X ∗
as in the previous Lemma and define
A : X →Km,x 7→ [f1(x), . . . , fm(x)]t .
If A : X →Km is given, define the corresponding linear subspaceLm := Ker(A).
Björn Bringmann Gelfand Widths in Compressive Sensing 9 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Connection with linear operators
Note that
dm(K ;X ) = inf
{sup
x∈K∩Ker(A)‖x‖ : A : X →Km linear, continuous
}.
If Lm is a subspace with codim(Lm)≤m, then choose f1, . . . , fm ∈ X ∗
as in the previous Lemma and define
A : X →Km,x 7→ [f1(x), . . . , fm(x)]t .
If A : X →Km is given, define the corresponding linear subspaceLm := Ker(A).
Björn Bringmann Gelfand Widths in Compressive Sensing 9 / 26
Introduction to m-Widths Kolmogorov and Gelfand Widths
Connection with linear operators
Note that
dm(K ;X ) = inf
{sup
x∈K∩Ker(A)‖x‖ : A : X →Km linear, continuous
}.
If Lm is a subspace with codim(Lm)≤m, then choose f1, . . . , fm ∈ X ∗
as in the previous Lemma and define
A : X →Km,x 7→ [f1(x), . . . , fm(x)]t .
If A : X →Km is given, define the corresponding linear subspaceLm := Ker(A).
Björn Bringmann Gelfand Widths in Compressive Sensing 9 / 26
Introduction to m-Widths Compressive m-widths
Compressive m-width
DefinitionThe compressive m-width of a subset K of a (real) Banach space X isdefined as
Em(K ;X ) := inf{
supx∈K‖x−∆(Ax)‖X : A ∈L (X ,Rm),∆: Rm→ X
}.
A is the measurement map and ∆: Rm→ X is the arbitrary reconstructionmap.
A
∆
Björn Bringmann Gelfand Widths in Compressive Sensing 10 / 26
Introduction to m-Widths Compressive m-widths
Adaptive compressive m-width
DefinitionThe adaptive map F : X → Rm is defined by
F (x) :=
λ1(x)
λ2;λ1(x)(x)...
λm;λ1(x),...,λm−1(x)(x)
.
for λ1(·),λ2;λ1(x)(·), . . . ,λm;λ1(x),...,λm−1(x)(·) ∈ X ∗. The adaptivecompressive m-width of a subset K of a Banach space X is defined as
Emada(K ;X ) := inf
{supx∈K‖x−∆(F (x))‖ : F : X → Rm adaptive,∆: Rm→ X
}
Björn Bringmann Gelfand Widths in Compressive Sensing 11 / 26
Introduction to m-Widths Compressive m-widths
Connection with the Gelfand m-width
TheoremIf K is a subset of a Banach space X, then
Emada(K ;X )≤ Em(K ;X ) .
If the subset K satisfies −K = K , then
dm(K ;X )≤ Emada(K ,X ) .
If the set K further satisfies K + K ⊂ a K for some positive constant a, then
Em(K ;X )≤ a dm(K ;X ) .
Therefore under these assumptions
1a
Em(K ;X )≤ dm(K ;X )≤ Emada(K ;X )≤ Em(K ;X ) .
Björn Bringmann Gelfand Widths in Compressive Sensing 12 / 26
Gelfand Widths of l1-balls
Outline
1 Introduction to m-Widths
Kolmogorov and Gelfand Widths
Compressive m-widths
2 Gelfand Widths of l1-balls
Upper bound
Lower bound
3 Applications
Optimal Number of Measurements
Kashin’s Decomposition Theorem
Björn Bringmann Gelfand Widths in Compressive Sensing 13 / 26
Gelfand Widths of l1-balls
Main result
TheoremFor 1 < p ≤ 2 and m < N, there exist constants c1,c2 > 0 depending onlyon p such that
c1 min{
1,ln(eN/m)
m
}1− 1p
≤ dm(BN1 , `
Np )≤ c2 min
{1,
ln(eN/m)
m
}1− 1p
.
Björn Bringmann Gelfand Widths in Compressive Sensing 14 / 26
Gelfand Widths of l1-balls Upper bound
Upper bound
TheoremThere is a constant C > 0 such that, for 1 < p ≤ 2 and m < N
dm(BN1 , `
Np )≤ C min
{1,
ln(eN/m)
m
}1− 1p
.
Björn Bringmann Gelfand Widths in Compressive Sensing 15 / 26
Gelfand Widths of l1-balls Lower bound
Lower bound
TheoremThere is a constant c > 0 such that, for 1 < p ≤ ∞ and m < N
dm(BN1 , `
Np )≥ c min
{1,
ln(eN/m)
m
}1− 1p
.
Björn Bringmann Gelfand Widths in Compressive Sensing 16 / 26
Gelfand Widths of l1-balls Lower bound
Recovery of 2s-sparse vectors
Theorem
Given a matrix A ∈ Rm×N , if every 2s-sparse vector x ∈ RN is a minimizerof ‖z‖1 subject to Az = Ax, then
m ≥ c1 s ln(
Nc2 s
)where c1 = 1
ln(9) and c2 = 4.
Björn Bringmann Gelfand Widths in Compressive Sensing 17 / 26
Gelfand Widths of l1-balls Lower bound
Preparation
LemmaGiven integers s < N, there exist
n ≥(
N4 s
)s/2
subsets S1, . . . ,Sn of [N] such that each Sj has cardinality s and
card(Si ∩Sj) <s2
whenever i 6= j .
Björn Bringmann Gelfand Widths in Compressive Sensing 18 / 26
Gelfand Widths of l1-balls Lower bound
Preparation
DefinitionLet X be a Banach space. For asubset T ⊂ X define the packingnumber P(T ,‖ · ‖X , t) for t > 0 by themaximum number P of pointsxk ∈ T ,k ∈ [P], which aret-separated, i.e. ‖xk −xl‖> t for allk , l ∈ [P],k 6= l .
Lemma
Then for any norm ‖ · ‖ on Rm thereholds
P(B1(0),‖ · ‖, t)≤(
1 +2t
)m
.
•
•
•
•
•
••
••
•
Björn Bringmann Gelfand Widths in Compressive Sensing 19 / 26
Gelfand Widths of l1-balls Lower bound
Preparation
DefinitionLet X be a Banach space. For asubset T ⊂ X define the packingnumber P(T ,‖ · ‖X , t) for t > 0 by themaximum number P of pointsxk ∈ T ,k ∈ [P], which aret-separated, i.e. ‖xk −xl‖> t for allk , l ∈ [P],k 6= l .
Lemma
Then for any norm ‖ · ‖ on Rm thereholds
P(B1(0),‖ · ‖, t)≤(
1 +2t
)m
.
•
•
•
•
•
••
••
•
Björn Bringmann Gelfand Widths in Compressive Sensing 19 / 26
Gelfand Widths of l1-balls Lower bound
Kolmogorov widths Revisited
Corollary
For 2≤ p < ∞ and m < N, there exist constants c1,c2 > 0 depending onlyon p such that the Kolmogorov widths satisfy
c1 min
{1,
ln( eN
m
)m
}1−1/p
≤ dm(BNp , `
N∞)≤ c2 min
{1,
ln( eN
m
)m
}1−1/p
Björn Bringmann Gelfand Widths in Compressive Sensing 20 / 26
Applications
Outline
1 Introduction to m-Widths
Kolmogorov and Gelfand Widths
Compressive m-widths
2 Gelfand Widths of l1-balls
Upper bound
Lower bound
3 Applications
Optimal Number of Measurements
Kashin’s Decomposition Theorem
Björn Bringmann Gelfand Widths in Compressive Sensing 21 / 26
Applications Optimal Number of Measurements
Estimates for the Compressive m-widths
Corollary
For 1 < p ≤ 2 and m < N, the adaptive and nonadaptive compressivem-widths satisfy
Emada(BN
1 , `Np )� Em(BN
1 , `Np )�min
{1,
ln(eN/m)
m
}1− 1p
.
Björn Bringmann Gelfand Widths in Compressive Sensing 22 / 26
Applications Optimal Number of Measurements
Optimal Number of Measurements
Theorem
Let 1 < p ≤ 2. Suppose that the matrix A ∈ Rm×N and the map∆ : Rm→ RN satisfy
‖x−∆(Ax)‖p ≤C
s1−1/pσs(x)1 ∀x ∈ RN .
Then for some constant c > 0 depending only on C there holds
m ≥ c s ln(
eNs
). (1)
In particular, if A ∈ Rm×N satisfies δ2s(A) < 0.6246, then necessarily (1)holds with c = c(δ2s).
Björn Bringmann Gelfand Widths in Compressive Sensing 23 / 26
Applications Optimal Number of Measurements
Donoho-Tanner Phase Transition
Figure: Success of L1−Minimization from Random Partial Fourier Measurements.x-Axis: δ = m/N undersampling fractiony-Axis: ρ = s/m sparsity fraction
Björn Bringmann Gelfand Widths in Compressive Sensing 24 / 26
Applications Kashin’s Decomposition Theorem
Kashin’s Decomposition Theorem
Theorem
There exist universal constants α,β > 0 such that, for any m ≥ 1 thespace R2m can be split into an orthogonal sum of two m-dimensionalsubspaces E and E⊥ such that
α√
m‖x‖2 ≤ ‖x‖1 ≤ β√
m‖x‖2
for all x ∈ E and for all x ∈ E⊥.
Björn Bringmann Gelfand Widths in Compressive Sensing 25 / 26
References
References
Holger Rauhut and Simon Foucart
A Mathematical Introduction to Compressive Sensing
Birkhäuser, 2013
Allan Pinkus
n-Widths in Approximation Theory
Springer, 1985
David L. Donoho and Jared Tanner
Observed Universality of Phase Transitions in High DimensionalGeometry, with Implications for Modern Data Analysis and SignalProcessing
Philosophical Transactions of the Royal Society, 2009Björn Bringmann Gelfand Widths in Compressive Sensing 26 / 26