S³ Seminar
January 20, 2017

# Claire Boyer

(LSTA, UPMC)

https://s3-seminar.github.io/seminars/claire-boyer

Title — Adapting to unknown noise level in super-resolution

Abstract — We study sparse spikes deconvolution over the space of complex-valued measures when the input measure is a finite sum of Dirac masses. We introduce a new procedure to handle the spike deconvolution when the noise level is unknown. Prediction and localization results will be presented for this approach. An insight on the probabilistic tools used in the proofs could be briefly given as well.

January 20, 2017

## Transcript

1. ### Spike deconvolution with unknown noise level Claire Boyer, Yohann de

Castro, Joseph Salmon, UPMC, LSTA, France January 20, 2017
2. ### 1 Introduction 2 The proposed approach Results Some words about

the proof 3 Numerical aspects 4 Numerical experiments
3. ### Introduction Motivations Point sources in applications Astronomy Microscopy (ﬂuorescent molecules)

Spectroscopy 2 / 37 Spike deconvolution
4. ### Introduction Motivations Point sources in applications Astronomy Microscopy (ﬂuorescent molecules)

Spectroscopy Oﬀ-the-grid Point sources do not live on a cartesian grid. 2 / 37 Spike deconvolution
5. ### Introduction What is spike deconvolution? Setting Aim: reconstruct a discrete

measure µ0 = s i=1 a0 i δt0 i supp(µ0) = {t0 1 , . . . , t0 s } ⊂ T (a0 i )1≤i≤s ∈ Cs 3 / 37 Spike deconvolution
6. ### Introduction What is spike deconvolution? Setting Aim: reconstruct a discrete

measure µ0 = s i=1 a0 i δt0 i supp(µ0) = {t0 1 , . . . , t0 s } ⊂ T (a0 i )1≤i≤s ∈ Cs From linear measurements y = Φµ0 + ε Φ is a ﬁlter ε is a complex Gaussian vector, ε = ε(1) + iε(2) with ε(i) ∼ N (0, σ2 0 Id) 3 / 37 Spike deconvolution
7. ### Introduction What is spike deconvolution? Setting Aim: reconstruct a discrete

measure µ0 = s i=1 a0 i δt0 i supp(µ0) = {t0 1 , . . . , t0 s } ⊂ T (a0 i )1≤i≤s ∈ Cs From linear measurements y = Φµ0 + ε Φ is a ﬁlter ε is a complex Gaussian vector, ε = ε(1) + iε(2) with ε(i) ∼ N (0, σ2 0 Id) In this work Low-pass ﬁlter : Φ = Fn Fn(µ) := (ck (µ))|k|≤fc , ck (µ) := T exp(−2πıkt)µ(dt) 3 / 37 Spike deconvolution
8. ### Introduction How to reconstruct µ0? 1-deconvolution: Beurling lasso (Blasso) min

µ 1 2 y − Fn (µ) 2 2 + λ µ TV µ TV = sup | j µ(Bj )| over all ﬁnite partitions (Bj ) of [0, 1] µ TV = s i=1 |ai | when the measure is discrete 4 / 37 Spike deconvolution
9. ### Introduction Beurling minimal extrapolation Beurling minimal extrapolation (1938) µ ∈

argmin µ µ TV s.t. T Φdµ = y with Φ = (ϕ1, . . . , ϕn ) Inﬁnite-dimensional variable µ Finitely many constraints 5 / 37 Spike deconvolution
10. ### Introduction Beurling minimal extrapolation Beurling minimal extrapolation (1938) µ ∈

argmin µ µ TV s.t. T Φdµ = y with Φ = (ϕ1, . . . , ϕn ) Inﬁnite-dimensional variable µ Finitely many constraints Fenchel dual program ˆ u ∈ arg max y, u s.t. n k=0 uk ϕk ∞ ≤ 1 Finite-dimensional variable Inﬁnitely many constraints Strong duality 5 / 37 Spike deconvolution
11. ### Introduction How to reconstruct µ0? 1-deconvolution: Beurling lasso (Blasso) min

µ 1 2 y − Fn (µ) 2 2 + λ µ TV µ TV = sup | j µ(Bj )| over all ﬁnite partitions (Bj ) of [0, 1] µ TV = s i=1 |ai | when the measure is discrete Algorithms proximal-based [Bredies, Pikkarainen 2012] root-ﬁnding [Candés, Fernandez-Granda 2012] 6 / 37 Spike deconvolution
12. ### Introduction How to reconstruct µ0? 1-deconvolution: Beurling lasso (Blasso) min

µ 1 2 y − Fn (µ) 2 2 + λ µ TV µ TV = sup | j µ(Bj )| over all ﬁnite partitions (Bj ) of [0, 1] µ TV = s i=1 |ai | when the measure is discrete Algorithms proximal-based [Bredies, Pikkarainen 2012] root-ﬁnding [Candés, Fernandez-Granda 2012] 6 / 37 Spike deconvolution
13. ### Introduction How to reconstruct µ0? 1-deconvolution: Beurling lasso (Blasso) min

µ 1 2 y − Fn (µ) 2 2 + λ µ TV µ TV = sup | j µ(Bj )| over all ﬁnite partitions (Bj ) of [0, 1] µ TV = s i=1 |ai | when the measure is discrete Algorithms proximal-based [Bredies, Pikkarainen 2012] root-ﬁnding [Candés, Fernandez-Granda 2012] Others based on Prony’s methods : MUSIC, ESPRIT, FRI 6 / 37 Spike deconvolution
14. ### Introduction How to reconstruct µ0? 1-deconvolution: Beurling lasso (Blasso) min

µ 1 2 y − Fn (µ) 2 2 + λ µ TV µ TV = sup | j µ(Bj )| over all ﬁnite partitions (Bj ) of [0, 1] µ TV = s i=1 |ai | when the measure is discrete Algorithms proximal-based [Bredies, Pikkarainen 2012] root-ﬁnding [Candés, Fernandez-Granda 2012] Others based on Prony’s methods : MUSIC, ESPRIT, FRI Link with 1-regularization ? 6 / 37 Spike deconvolution
15. ### Introduction Link to compressed sensing Spike deconvolution Compressed sensing Setting

∞-dim ﬁnite-dim (or easy ∞-dim) Object of interest a discrete measure a sparse signal (mainly) µ = s i=1 ai δti x ∈ Cn 1illustrations from Carlos Fernandez-Granda 7 / 37 Spike deconvolution
16. ### Introduction Link to compressed sensing Spike deconvolution Compressed sensing Setting

∞-dim ﬁnite-dim (or easy ∞-dim) Object of interest a discrete measure a sparse signal (mainly) µ = s i=1 ai δti x ∈ Cn Measurements low-pass ﬁlter random 1 spectrum extrapolation spectrum interpolation 1illustrations from Carlos Fernandez-Granda 7 / 37 Spike deconvolution
17. ### Introduction Link to compressed sensing Spike deconvolution Compressed sensing Setting

∞-dim ﬁnite-dim (or easy ∞-dim) Object of interest a discrete measure a sparse signal (mainly) µ = s i=1 ai δti x ∈ Cn Measurements low-pass ﬁlter random 1 spectrum extrapolation spectrum interpolation Resolution minµ 1 2 y − Fn (µ) 2 2 + λ µ TV minx 1 2 y − Frd (x) 2 2 + λ x 1 Beurling Lasso estimator Lasso Key feature Minimum separation Measurement incoherence + degree of sparsity 1illustrations from Carlos Fernandez-Granda 7 / 37 Spike deconvolution
18. ### Introduction Link to compressed sensing Spike deconvolution Compressed sensing Setting

∞-dim ﬁnite-dim (or easy ∞-dim) Object of interest a discrete measure a sparse signal (mainly) µ = s i=1 ai δti x ∈ Cn Measurements low-pass ﬁlter random 1 spectrum extrapolation spectrum interpolation Resolution minµ 1 2 y − Fn (µ) 2 2 + λ µ TV minx 1 2 y − Frd (x) 2 2 + λ x 1 Beurling Lasso estimator Lasso Key feature Minimum separation Measurement incoherence + degree of sparsity Algorithm Root-ﬁnding (...) Easy convex programming 1illustrations from Carlos Fernandez-Granda 7 / 37 Spike deconvolution
19. ### Introduction Contributions Handling unknown noise level 8 / 37 Spike

deconvolution
20. ### Introduction Contributions Handling unknown noise level Assessing the noise level

using the Rice method for a non-Gaussian process 8 / 37 Spike deconvolution
21. ### Introduction Contributions Handling unknown noise level Assessing the noise level

using the Rice method for a non-Gaussian process Prediction & strong localization accuracy 8 / 37 Spike deconvolution
22. ### Introduction Contributions Handling unknown noise level Assessing the noise level

using the Rice method for a non-Gaussian process Prediction & strong localization accuracy Numerics : the root-ﬁnding can always be done (open question of [1, Eq. (4.5)]) E. J. Candès and C. Fernandez-Granda. Towards a mathematical theory of super-resolution. Communications on Pure and Applied Mathematics, 67(6):906–956, 2014. 8 / 37 Spike deconvolution
23. ### Introduction Contributions Handling unknown noise level Assessing the noise level

using the Rice method for a non-Gaussian process Prediction & strong localization accuracy Numerics : the root-ﬁnding can always be done (open question of [1, Eq. (4.5)]) Claire Boyer, Yohann de Castro, and Joseph Salmon. Adapting to unknown noise level in sparse deconvolution. Information and Inference, abs/1606.04760, 2016. E. J. Candès and C. Fernandez-Granda. Towards a mathematical theory of super-resolution. Communications on Pure and Applied Mathematics, 67(6):906–956, 2014. 8 / 37 Spike deconvolution
24. ### 1 Introduction 2 The proposed approach Results Some words about

the proof 3 Numerical aspects 4 Numerical experiments
25. ### The proposed approach The proposed method Data y = Fnµ0

+ ε y ∈ Cn with n = 2fc + 1 ε is a complex Gaussian vector, ε = ε(1) + iε(2) with ε(j) ∼ N(0, σ2 0 Id) 9 / 37 Spike deconvolution
26. ### The proposed approach The proposed method Data y = Fnµ0

+ ε y ∈ Cn with n = 2fc + 1 ε is a complex Gaussian vector, ε = ε(1) + iε(2) with ε(j) ∼ N(0, σ2 0 Id) Handling unknown noise level : CBLasso (ˆ µ, ˆ σ) ∈ argmin (µ,σ)∈E∗×R++ 1 2nσ y − Fn (µ) 2 2 + σ 2 + λ µ TV , Owen 07, Antoniadis 10, Belloni Chernozhukov Wang 11, Sun Zhang 12, van de Geer 15 : square-root lasso and scaled-lasso. 9 / 37 Spike deconvolution
27. ### The proposed approach Where is the square-root? Why square-root? The

CBLasso reads ([2] ∼ scaled lasso) (ˆ µ, ˆ σ) ∈ argmin (µ,σ)∈E∗×R++ 1 2nσ y − Fn (µ) 2 2 + σ 2 + λ µ TV . When the solution is reached for ˆ σ > 0, ([1] ∼ square-root lasso) ˆ σ = y − Fn (ˆ µ) 2/ √ n ˆ µ ∈ argmin µ∈E∗ y − Fn (µ) 2/ √ n + λ µ TV A. Belloni, V. Chernozhukov, and L. Wang. Square-root Lasso: Pivotal recovery of sparse signals via conic programming. Biometrika, 98(4):791–806, 2011. T. Sun and C.-H. Zhang. Scaled sparse linear regression. Biometrika, 99(4):879–898, 2012. 10 / 37 Spike deconvolution
28. ### The proposed approach Compatibility limits Suﬃcient conditions for oracle inequalities

RIP =⇒ REC =⇒ Compatibility Compatibility condition : C(L, S) > 0 C(L, S) := inf |S| Fn(ν) 2 2 /n s.t. supp(ν) = S, νS TV = 1, νSc TV ≤ L . Consider ν as a diﬀerence of two measures: ˆ µ and µ0 11 / 37 Spike deconvolution
29. ### The proposed approach Compatibility limits Suﬃcient conditions for oracle inequalities

RIP =⇒ REC =⇒ Compatibility Compatibility condition : C(L, S) > 0 C(L, S) := inf |S| Fn(ν) 2 2 /n s.t. supp(ν) = S, νS TV = 1, νSc TV ≤ L . Consider ν as a diﬀerence of two measures: ˆ µ and µ0 Compatibility does not hold νε = δ−ε + δε @@@@@ Compatibility =⇒ \$\$ REC =⇒ ¨ ¨ RIP highly coherent designs: close Dirac masses share almost the same Fourier coeﬃcients Non-uniform approach Measure-dependent reconstruction 11 / 37 Spike deconvolution
30. ### The proposed approach – Results Standard assumptions Assumption (Sampling rate

condition). λSNR ≤ √ 17 − 4 2 0.0616 with SNR := µ0 TV E[ ε 2 2 ]/n = µ0 TV √ 2σ0 . λ ≥ 2 2 log n/n =⇒ n/log n ≥ C SNR2 12 / 37 Spike deconvolution
31. ### The proposed approach – Results Standard assumptions Assumption (Sampling rate

condition). λSNR ≤ √ 17 − 4 2 0.0616 with SNR := µ0 TV E[ ε 2 2 ]/n = µ0 TV √ 2σ0 . λ ≥ 2 2 log n/n =⇒ n/log n ≥ C SNR2 Assumption (No-overﬁtting). ˆ σ > 0 12 / 37 Spike deconvolution
32. ### The proposed approach – Results Standard assumptions Assumption (Sampling rate

condition). λSNR ≤ √ 17 − 4 2 0.0616 with SNR := µ0 TV E[ ε 2 2 ]/n = µ0 TV √ 2σ0 . λ ≥ 2 2 log n/n =⇒ n/log n ≥ C SNR2 Assumption (No-overﬁtting). ˆ σ > 0 Assumption (Separation condition). For supp(µ0) = {t0 1 , . . . , t0 s0 }, ∀i = j, d(t0 i , t0 j ) ≥ 1.26 fc 12 / 37 Spike deconvolution
33. ### The proposed approach – Results Prediction result Theorem (B., de

Castro, Salmon, 2016). Let C > 2 √ 2. Set C > 0, that may depend on C. Assume the sampling rate condition, the separation condition. The estimator ˆ µ solution to CBLasso with a choice λ ≥ C log n/n satisﬁes 1 n Fn (ˆ µ − µ0) 2 2 ≤ C s0 λ2 σ0, with high probability. 13 / 37 Spike deconvolution
34. ### The proposed approach – Results Prediction result Theorem (B., de

Castro, Salmon, 2016). Let C > 2 √ 2. Set C > 0, that may depend on C. Assume the sampling rate condition, the separation condition. The estimator ˆ µ solution to CBLasso with a choice λ ≥ C log n/n satisﬁes 1 n Fn (ˆ µ − µ0) 2 2 ≤ C s0 λ2 σ2 0 = O s0σ2 0 log n n , with high probability. "fast rate of convergence" of [1] G. Tang, B. N. Bhaskar, and B. Recht. Near minimax line spectral estimation. Information Theory, IEEE Transactions on, 61(1):499–512, 2015. 13 / 37 Spike deconvolution
35. ### The proposed approach – Results Localization results Near regions ∀j

∈ [s0 ], Nj := t ∈ [0, 1]; d(t, t0 j ) ≤ c1 fc , with 0 < c1 < 1.26/2. Far region F := [0, 1] \ j∈[s0] Nj 14 / 37 Spike deconvolution
36. ### The proposed approach – Results Localization results Near regions ∀j

∈ [s0 ], Nj := t ∈ [0, 1]; d(t, t0 j ) ≤ c1 fc , with 0 < c1 < 1.26/2. Far region F := [0, 1] \ j∈[s0] Nj Theorem (B., de Castro, Salmon, 2016). Let C > 2 √ 2. Let C > 0 may depend on C. The estimator ˆ µ, solution to CBLasso with a choice λ ≥ C log n/n, satisﬁes 1 ∀j ∈ [s0], a0 j − {k: ˆ tk ∈Nj } ˆ ak ≤ C σ0s0 log n/n , 14 / 37 Spike deconvolution
37. ### The proposed approach – Results Localization results Near regions ∀j

∈ [s0 ], Nj := t ∈ [0, 1]; d(t, t0 j ) ≤ c1 fc , with 0 < c1 < 1.26/2. Far region F := [0, 1] \ j∈[s0] Nj Theorem (B., de Castro, Salmon, 2016). Let C > 2 √ 2. Let C > 0 may depend on C. The estimator ˆ µ, solution to CBLasso with a choice λ ≥ C log n/n, satisﬁes 1 ∀j ∈ [s0], a0 j − {k: ˆ tk ∈Nj } ˆ ak ≤ C σ0s0 log n/n , 2 ∀j ∈ [s0], {k: ˆ tk ∈Nj } |ˆ ak |d2(t0 j , ˆ tk ) ≤ C σ0s0 log n/n/n2 , 14 / 37 Spike deconvolution
38. ### The proposed approach – Results Localization results Near regions ∀j

∈ [s0 ], Nj := t ∈ [0, 1]; d(t, t0 j ) ≤ c1 fc , with 0 < c1 < 1.26/2. Far region F := [0, 1] \ j∈[s0] Nj Theorem (B., de Castro, Salmon, 2016). Let C > 2 √ 2. Let C > 0 may depend on C. The estimator ˆ µ, solution to CBLasso with a choice λ ≥ C log n/n, satisﬁes 1 ∀j ∈ [s0], a0 j − {k: ˆ tk ∈Nj } ˆ ak ≤ C σ0s0 log n/n , 2 ∀j ∈ [s0], {k: ˆ tk ∈Nj } |ˆ ak |d2(t0 j , ˆ tk ) ≤ C σ0s0 log n/n/n2 , 3 {k: ˆ tk ∈F} |ˆ ak | ≤ C σ0s0 log n/n , with high probability. 14 / 37 Spike deconvolution
39. ### The proposed approach – Results Localization results Corollary (B., de

Castro, Salmon, 2016). For any t0 j in the support of µ0 such that a0 j > C σ0 s0λ, there exists an element ˆ tk in the support of ˆ µ such that d(t0 j , ˆ tk ) ≤ C σ0 s0λ |a0 j | − C σ0 s0λ 1 n , with high probability. independent of the other spikes magnitude 15 / 37 Spike deconvolution
40. ### The proposed approach – Results Noise level estimation Proposition (B.,

de Castro, Salmon, 2016). Under the sampling rate assumption, if n > −8 log(α), √ nˆ σ ε 2 − 1 ≤ 1 2 , with probability larger than 1 − α 2 √ 2 n + 2 √ 3+3 3 . 16 / 37 Spike deconvolution
41. ### The proposed approach – Some words about the proof Sketch

of proof KKT condition 1 n F∗ n (y − Fn (ˆ µ)) = ˆ σλˆ p ˆ p ∞ ≤ 1 R T ˆ p(t)ˆ µ(dt) = ˆ µ TV (ˆ p subgradient of · TV at ˆ µ) 17 / 37 Spike deconvolution
42. ### The proposed approach – Some words about the proof Sketch

of proof KKT condition 1 n F∗ n (y − Fn (ˆ µ)) = ˆ σλˆ p ˆ p ∞ ≤ 1 R T ˆ p(t)ˆ µ(dt) = ˆ µ TV (ˆ p subgradient of · TV at ˆ µ) 17 / 37 Spike deconvolution
43. ### The proposed approach – Some words about the proof Sketch

of proof KKT condition 1 n F∗ n (y − Fn (ˆ µ)) = ˆ σλˆ p ˆ p ∞ ≤ 1 R T ˆ p(t)ˆ µ(dt) = ˆ µ TV (ˆ p subgradient of · TV at ˆ µ) Dual certiﬁcate Construction of q(t) = fc k=−fc ck e2iπkt obeying to q(tj ) = v0 j := a0 j |a0 j | ∀tj ∈ supp µ0 |q(t)| < 1 ∀t / ∈ supp µ0 17 / 37 Spike deconvolution
44. ### The proposed approach – Some words about the proof Sketch

of proof KKT condition 1 n F∗ n (y − Fn (ˆ µ)) = ˆ σλˆ p ˆ p ∞ ≤ 1 R T ˆ p(t)ˆ µ(dt) = ˆ µ TV (ˆ p subgradient of · TV at ˆ µ) Dual certiﬁcate Construction of q(t) = fc k=−fc ck e2iπkt obeying to q(tj ) = v0 j := a0 j |a0 j | ∀tj ∈ supp µ0 q (tj ) = 0 |q(t)| < 1 ∀t / ∈ supp µ0 17 / 37 Spike deconvolution
45. ### The proposed approach – Some words about the proof Sketch

of proof Spike localization Proofs are based on the work of [1, 2, 3] amended by Rice method Prediction adapt the proof of [3] to our setting Rice method for a non-Gaussian process J.-M. Azaïs, Y. De Castro, and F. Gamboa. Spike detection from inaccurate samplings. Applied and Computational Harmonic Analysis, 38(2):177–195, 2015. C. Fernandez-Granda. Support detection in super-resolution. arXiv preprint arXiv:1302.3921, 2013. G. Tang, B. N. Bhaskar, and B. Recht. Near minimax line spectral estimation. Information Theory, IEEE Transactions on, 61(1):499–512, 2015. 18 / 37 Spike deconvolution
46. ### 1 Introduction 2 The proposed approach Results Some words about

the proof 3 Numerical aspects 4 Numerical experiments
47. ### Numerical aspects Primal-dual Proposition. Denoting Dn = c ∈ Cn

: F∗ n (c) ∞ ≤ 1, nλ2 c 2 ≤ 1 , the dual formulation of the CBLasso reads ˆ c ∈ arg max c∈Dn λ y, c . (1) Then, we have the link-equation between primal and dual solutions y = nλˆ σˆ c + Fn (ˆ µ) . (2) as well as a link between the coeﬃcient and the polynomial F∗ n (ˆ c) = ˆ p . (3) 19 / 37 Spike deconvolution
48. ### Numerical aspects Primal-dual Proposition. Denoting Dn = c ∈ Cn

: F∗ n (c) ∞ ≤ 1, nλ2 c 2 ≤ 1 , the dual formulation of the CBLasso reads ˆ c ∈ arg max c∈Dn λ y, c . (1) Then, we have the link-equation between primal and dual solutions y = nλˆ σˆ c + Fn (ˆ µ) . (2) as well as a link between the coeﬃcient and the polynomial F∗ n (ˆ c) = ˆ p . (3) If the dual polynomial ˆ p associated is not constant the support of ˆ µ is ﬁnite, the support of ˆ µ is included in the set of its derivative roots, i.e. where the polynomial saturates at 1. the proof based on a dual certiﬁcate construction is possible. 19 / 37 Spike deconvolution
49. ### Numerical aspects Primal-dual : comparison with Blasso Dual of the

Blasso ˆ c ∈ arg max F∗ n (c) ∞≤1 y, c . Dual of the CBLasso ˆ c ∈ arg max F∗ n (c) ∞≤1 nλ2 c 2≤1 λ y, c . 20 / 37 Spike deconvolution
50. ### Numerical aspects Primal-dual : comparison with Blasso Dual of the

Blasso ˆ c ∈ arg max F∗ n (c) ∞≤1 y, c . Open question When the dual polynomial is non- constant ? Dual of the CBLasso ˆ c ∈ arg max F∗ n (c) ∞≤1 nλ2 c 2≤1 λ y, c . For the CBLasso We answer this. 20 / 37 Spike deconvolution
51. ### Numerical aspects Showing that the dual polynomial is non-constant |ˆ

p|2 is of constant modulus 1 ⇒ ˆ p = vϕk with v ∈ C and ϕk (·) = exp(2πık·) for some −fc ≤ k ≤ fc . if |v| < 1, using Holder’s inequality on R T ˆ p(t)ˆ µ(dt) = ˆ µ TV leads to ˆ µ = 0. if |v| = 1, we also have ˆ c ∈ Dn, so ˆ c 2 ≤ 1/( √ nλ), leading to |v| ≤ 1/( √ nλ). Since λ > 2 √ logn/ √ n ⇒ |v| < 1, which contradicts |v| = 1. One can then conclude that a dual polynomial of constant modulus never occurs in the CBLasso setup 21 / 37 Spike deconvolution
52. ### Numerical aspects Discussion on the value of λ 22 /

37 Spike deconvolution
53. ### Numerical aspects SDP formulation of the CBLasso One can represent

the dual feasible set Dn as an SDP condition. The dual problem can be cast as follows max c∈Cn λ y, c such that ∃Q ∈ Cn×n Q c c∗ 1 0 n−j i=1 Qi,i+j = 1 if j = 0 0 j = 1, . . . , n − 1. 23 / 37 Spike deconvolution
54. ### Numerical aspects SDP formulation of the CBLasso One can represent

the dual feasible set Dn as an SDP condition. The dual problem can be cast as follows max c∈Cn λ y, c such that ∃Q ∈ Cn×n Q c c∗ 1 0 n−j i=1 Qi,i+j = 1 if j = 0 0 j = 1, . . . , n − 1. In λ √ nc λ √ nc∗ 1 0 23 / 37 Spike deconvolution
55. ### Numerical aspects Proposed algorithm Algorithm Given the data y ∈

Cn 1 solve the dual problem to ﬁnd the coeﬃcients ˆ c of the dual polynomial ˆ p (cvx Matlab toolbox); 24 / 37 Spike deconvolution
56. ### Numerical aspects Proposed algorithm Algorithm Given the data y ∈

Cn 1 solve the dual problem to ﬁnd the coeﬃcients ˆ c of the dual polynomial ˆ p (cvx Matlab toolbox); 2 identify supp(ˆ µ) = {ˆ t1, . . . , ˆ tˆ s } using the roots of 1 − |ˆ p|2; 24 / 37 Spike deconvolution
57. ### Numerical aspects Proposed algorithm Algorithm Given the data y ∈

Cn 1 solve the dual problem to ﬁnd the coeﬃcients ˆ c of the dual polynomial ˆ p (cvx Matlab toolbox); 2 identify supp(ˆ µ) = {ˆ t1, . . . , ˆ tˆ s } using the roots of 1 − |ˆ p|2; 3 ﬁx the design matrix X ∈ Cn×ˆ s , deﬁned by Xk,j = ϕk (ˆ tj ); 24 / 37 Spike deconvolution
58. ### Numerical aspects Proposed algorithm Algorithm Given the data y ∈

Cn 1 solve the dual problem to ﬁnd the coeﬃcients ˆ c of the dual polynomial ˆ p (cvx Matlab toolbox); 2 identify supp(ˆ µ) = {ˆ t1, . . . , ˆ tˆ s } using the roots of 1 − |ˆ p|2; 3 ﬁx the design matrix X ∈ Cn×ˆ s , deﬁned by Xk,j = ϕk (ˆ tj ); 4 solve now the following ﬁnite-dimensional problem (ˆ a, ˆ σ) ∈ argmin (a,σ)∈Cˆ s ×R++ 1 2nσ y − Xa 2 2 + σ 2 + λ a 1 , 24 / 37 Spike deconvolution
59. ### Numerical aspects Proposed algorithm Algorithm Given the data y ∈

Cn 1 solve the dual problem to ﬁnd the coeﬃcients ˆ c of the dual polynomial ˆ p (cvx Matlab toolbox); 2 identify supp(ˆ µ) = {ˆ t1, . . . , ˆ tˆ s } using the roots of 1 − |ˆ p|2; 3 ﬁx the design matrix X ∈ Cn×ˆ s , deﬁned by Xk,j = ϕk (ˆ tj ); 4 solve now the following ﬁnite-dimensional problem (ˆ a, ˆ σ) ∈ argmin (a,σ)∈Cˆ s ×R++ 1 2nσ y − Xa 2 2 + σ 2 + λ a 1 , as follows: for an initial value of ˆ σ, until some stopping criterion, alternate the following steps solve the previous problem for a ﬁxed ˆ σ to compute ˆ a, ˆ a = X+y − λˆ σ(X∗X)−1 sign(X∗ ˆ c) update ˆ σ = y − X ˆ a 2/ √ n using the new value of ˆ a, 24 / 37 Spike deconvolution
60. ### 1 Introduction 2 The proposed approach Results Some words about

the proof 3 Numerical aspects 4 Numerical experiments
61. ### Numerical experiments Source code Code available at https://github.com/claireBoyer/CBLasso http://www.lsta.upmc.fr/boyer/ 25

/ 37 Spike deconvolution
62. ### Numerical experiments Measure reconstruction 0 0.2 0.4 0.6 0.8 1

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Original BLasso CBLasso Reconstruction of a discrete measure. The original measure µ0 is composed of 3 spikes (in black). The reconstructed measure ˆ µ using our proposed CBLasso (in blue). In comparison, we plot the reconstructed measure using the BLasso, (in red). 26 / 37 Spike deconvolution
63. ### Numerical experiments Noise level estimation ε = ε(1) + iε(2)

with ε(j) ∼ N(0, σ0 Id) with σ0 = 1/ √ 2. 27 / 37 Spike deconvolution
64. ### Numerical experiments Noise level estimation ε = ε(1) + iε(2)

with ε(j) ∼ N(0, σ0 Id) with σ0 = 1/ √ 2. 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 BLasso CBLasso Boxplot on ˆ σ for 100 CBLasso consistent estimations of √ 2σ0 = 1. We compare our method to ˆ σBLasso = y − Fn (ˆ µBLasso) 2 √ n − ˆ sBLasso proposed in [1] where ˆ µBLasso is the reconstructed measure supported on ˆ sBLasso spikes via BLasso. S. Reid, R. Tibshirani, and J. Friedman. A study of error variance estimation in lasso regression. arXiv preprint arXiv:1311.5274, 2014. 27 / 37 Spike deconvolution
65. ### Numerical experiments Bias Noise level estimation √ nˆ σ ε

2 − 1 ≤ 1 2 , with high probability. Bias ˆ σ √ 2σ0 × E g 2 √ 2n = √ 2σ0 × Γ(n + 1/2) √ nΓ(n) → √ 2σ0 , showing that ˆ σ/ √ 2 is a consistent estimator of σ0 . 28 / 37 Spike deconvolution
66. ### Numerical experiments Conclusion The CBLasso new approach to handle unknown

noise level in spike detection theoretical contributions : prediction for CBLasso localization for CBLasso closing the question of constant polynomial in this setting numerical method is also proposed to tackle the CBLasso 29 / 37 Spike deconvolution
67. ### Numerical experiments Conclusion The CBLasso new approach to handle unknown

noise level in spike detection theoretical contributions : prediction for CBLasso localization for CBLasso closing the question of constant polynomial in this setting numerical method is also proposed to tackle the CBLasso Claire Boyer, Yohann de Castro, and Joseph Salmon. Adapting to unknown noise level in sparse deconvolution. Information and Inference, abs/1606.04760, 2016. 29 / 37 Spike deconvolution

69. ### Discussion on λ Discussion on λ λmax(y) = F∗ n

(y) ∞/ √ n y 2 (for Blasso F∗ n (y) ∞/n).
70. ### Proposition. Deﬁning λmin (y) = 1/( ˆ c(BME) 2 √

n) and the problem ˆ µ(BME) ∈ argmin Fn(µ)=y µ TV , (4) and its dual formulation ˆ c(BME) ∈ arg max c∈Cn y, c s.t. F∗ n (c) ∞ ≤ 1 . (5) the following statements are equivalent (i) λ ∈]0, λmin(y)], (ii) ˆ c = ˆ c(BME), (iii) ˆ σ = 0 (overﬁtting). Remark that λmin (y) = 1/( ˆ c(BME) 2 √ n) > 1/ √ n.
71. ### Proof tricks Rice method One needs to upper bound the

probabilities of the following events { F∗ n (ε) ∞ ≤ λ} and F∗ n (ε) ∞ √ n ε 2 ≤ R , where F∗ n (ε)(t) = fc k=−fc εk exp(2πıkt) , for all t ∈ [0, 1]. The ﬁrst event can be handled with a Rice formula for stationary Gaussian processes as in [1]. Due to the presence of the denominator, the second event cannot be described by a Gaussian process and its analysis is a bit more involved.
72. ### Proof tricks Rice method : notation We start with some

notation. Let z(1) = (z(1) −fc , . . . , z(1) 0 , . . . , z(1) fc ), z(2) = (z(2) −fc , . . . , z(2) 0 , . . . , z(2) fc ) , be i.i.d Nn (0, Idn ) random vectors. Set, for any t ∈ [0, 1], X(t) = z(1) 0 + fc k=1 (z(1) k + z(1) −k ) cos(2πkt) + fc k=1 (z(2) −k − z(2) k ) sin(2πkt) , Y (t) = z(2) 0 + fc k=1 (z(2) k + z(2) −k ) cos(2πkt) + fc k=1 (z(1) k − z(1) −k ) sin(2πkt) , Z(t) = X(t) + ıY (t) . Then, note that σ0 Z ∞ d = F∗ n (ε) ∞ and sup t∈[0,1] |Z(t)| √ n( z(1) 2 2 + z(2) 2 2 )1 2 d = F∗ n (ε) ∞ √ n ε 2 , where σ0 > 0 is the (unknown) standard deviation of the noise ε.
73. ### Proof tricks For the Gaussian process Lemma 1. For a

complex valued centered Gaussian random vector ε as deﬁned in (??), it holds ∀u > 0, P { F∗ n (ε) ∞ > u} ≤ n exp − u2 4nσ2 0 , where σ0 > 0 denotes the noise level. Observe that X(t) and Y (t) are two independent stationary Gaussian processes with the same auto-covariance function Σ given by ∀t ∈ [0, 1], Σ(t) = 1 + 2 fc k=1 cos(2πkt) =: Dfc (t) , where Dfc denotes the Dirichlet kernel. Set σn 2 = var X(t) = Dfc (0) = n . (6)
74. ### Proof tricks For the Gaussian process We use the following

inequalities, for any u > 0, P{ Z ∞ > u} ≤ P{ X ∞ > u/ √ 2}+P{ Y ∞ > u/ √ 2} = 2P{ X ∞ > u/ √ 2} , (7) and (by symmetry of the process X) P{ X ∞ > u/ √ 2} ≤ 2P{ sup t∈[0,1] X(t) > u/ √ 2} . (8) To give bounds to the right hand side of (8), we use the Rice method (see Azais & Wschebor [page 93]) P{ sup t∈[0,1] X(t) > u/ √ 2} = P{∀t ∈ [0, 1]; X(t) > u/ √ 2} + P{U u/ √ 2 > 0} , ≤ 1 √ 2π +∞ u/( √ 2σn) exp − v2 2 d + E(U u/ √ 2 ) , where Uv is the number of up-crossings of the level v by the process X(t) on the interval [0, 1]. By the Rice formula E(U u/ √ 2 ) = 1 2π var X (t)) 1 σn exp − u2 4σn 2 , where var X (t) = −Σ (0) = 2(2π)2 fc k=1 k2 = 4π2 3 fc (fc + 1)n . (9)
75. ### Proof tricks For the Gaussian process A Chernoﬀ argument provides

for any w > 0, +∞ w exp(−v2/2)d/ √ 2π ≤ exp(−w2/2), which yields P sup t∈[0,1] X(t) > u √ 2 ≤ exp − u2 4n + fc (fc + 1) 3 exp − u2 4n ≤ n 2 exp − u2 4n . The result follows with (8).