Sparse Polynomial Interpolation and Berlekamp/Massey algorithms that correct Outlier Errors in Input Values

Transcription

1 Sparse Polynomial Interpolation and Berlekamp/Massey algorithms that correct Outlier Errors in Input Values Clément PERNET joint work with Matthew T. COMER and Erich L. KALTOFEN : LIG/INRIA-MOAIS, Grenoble Université, France : North Carolina State University, USA ISSAC 12, Grenoble, France, July 23rd, 2012

2 Outline Berlekamp/Massey algorithm with errors Bounds on the decoding capacity Decoding algorithms Sparse Polynomial Interpolation with errors Relations to Reed-Solomon decoding

3 Introduction Polynomial interpolation with errors Dense case: Reed-Solomon codes / CRT codes number of evaluation points made adaptive on error impact and degree [Khonji & Al. 10]

4 Introduction Polynomial interpolation with errors Dense case: Reed-Solomon codes / CRT codes number of evaluation points made adaptive on error impact and degree [Khonji & Al. 10] Sparse case: Present work based on Ben-Or & Tiwari s interpolation algorithm itself based on Berlekamp/Massey algorithm develop Berlekamp/Massey Algorithm with errors

5 Preliminaries Linear recurring sequences Sequence (a 0, a 1,..., a n,... ) such that t 1 j 0 a j+t = λ i a i+j generating polynomial: Λ(z) = z t t 1 i=0 λ iz i minimal generating polynomial: Λ(z) of minimal degree i=0 linear complexity of (a i ) i : the minimal degree of Λ Hamming weight: weight(x) = #{i x i 0} Hamming distance: d H (x, y) = weight(x y)

6 Berlekamp/Massey algorithm Input: (a 0,..., a n 1 ) a sequence of field elements. Result: Λ(z) = L n i=0 λ iz i a monic polynomial of minimal degree L n n such that L n i=0 λ ia i+j = 0 for j = 0,..., n L n 1. Guarantee : BMA finds Λ of degree t from 2t entries.

7 Outline Berlekamp/Massey algorithm with errors Bounds on the decoding capacity Decoding algorithms Sparse Polynomial Interpolation with errors Relations to Reed-Solomon decoding

8 Problem Statement Berlkamp/Massey with errors Suppose (a 0, a 1,... ) is linearly generated by Λ(z) of degree t where Λ(0) 0. Given (b 0, b 1,... ) = (a 0, a 1,... ) + ε, where weight(ε) E: 1. How to recover Λ(z) and (a 0, a 1,... ) 2. How many entries required for a unique solution a list of solutions including (a0, a 1,... )

9 Problem Statement Berlkamp/Massey with errors Suppose (a 0, a 1,... ) is linearly generated by Λ(z) of degree t where Λ(0) 0. Given (b 0, b 1,... ) = (a 0, a 1,... ) + ε, where weight(ε) E: 1. How to recover Λ(z) and (a 0, a 1,... ) 2. How many entries required for a unique solution a list of solutions including (a0, a 1,... ) Coding Theory formulation Let C be the set of all sequences of linear complexity t. 1. How to decode C? 2. What are the best correction capacity? for unique decoding list decoding

10 How many entries to guarantee uniqueness? Case E = 1, t = 2 (a i ) Λ(z) (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 2 2z 2 + z 4 + z 6 Where is the error?

11 How many entries to guarantee uniqueness? Case E = 1, t = 2 (a i ) Λ(z) (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 2 2z 2 + z 4 + z 6 (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 1 + z 2 Where is the error?

12 How many entries to guarantee uniqueness? Case E = 1, t = 2 (a i ) Λ(z) (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 2 2z 2 + z 4 + z 6 (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 1 + z 2 (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 1 + z 2 Where is the error?

13 How many entries to guarantee uniqueness? Case E = 1, t = 2 (a i ) Λ(z) (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 2 2z 2 + z 4 + z 6 (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 1 + z 2 (0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0) 1 + z 2 Where is the error? A unique solution is not guaranteed with t = 2, E = 1 and n = 11 Is n 2t(2E + 1) a necessary condition?

14 Generalization to any E 1 t 1 times {}}{ Let 0 = ( 0,..., 0). Then s = (0, 1, 0, 1, 0, 1, 0, 1) is generated by z t 1 or z t + 1 up to E = 1 error. Then E times {}}{ ( s, s,..., s, 0, 1, 0) is generated by z t 1 or z t + 1 up to E errors. ambiguity with n = 2t(2E + 1) 1 values.

15 Generalization to any E 1 t 1 times {}}{ Let 0 = ( 0,..., 0). Then s = (0, 1, 0, 1, 0, 1, 0, 1) is generated by z t 1 or z t + 1 up to E = 1 error. Then E times {}}{ ( s, s,..., s, 0, 1, 0) is generated by z t 1 or z t + 1 up to E errors. ambiguity with n = 2t(2E + 1) 1 values. Theorem Necessary condition for unique decoding: n 2t(2E + 1)

16 The Majority Rule Berlekamp/Massey algorithm 2t E=2 n=2t(2e+1) Λ Λ Λ Λ Λ

17 The Majority Rule Berlekamp/Massey algorithm 2t E=2 n=2t(2e+1) Λ Λ Λ Λ Λ Input: (a 0,..., a n 1 ) + ε, where n = 2t(2E + 1), weight(ε) E, and (a 0,..., a n 1 ) minimally generated by Λ of degree t, where Λ(0) 0. Output: Λ(z) and (a 0,..., a n 1 ). 1 begin 2 Run BMA on 2E + 1 segments of 2t entries and record Λ i (z) on each segment; 3 Perform majority vote to find Λ(z);

18 The Majority Rule Berlekamp/Massey algorithm 2t E=2 n=2t(2e+1) Λ Λ Λ Λ Λ Input: (a 0,..., a n 1 ) + ε, where n = 2t(2E + 1), weight(ε) E, and (a 0,..., a n 1 ) minimally generated by Λ of degree t, where Λ(0) 0. Output: Λ(z) and (a 0,..., a n 1 ). 1 begin 2 Run BMA on 2E + 1 segments of 2t entries and record Λ i (z) on each segment; 3 Perform majority vote to find Λ(z); 4 Use a clean segment to clean-up the sequence ; 5 return Λ(z) and (a 0, a 1,... );

19 Algorithm SequenceCleanUp Input: Λ(z) = z t + t 1 i=0 λ ix i where Λ(0) 0 Input: (a 0,..., a n 1 ), where n t + 1 Input: E, the maximum number of corrections to make Input: k, such that (a k, a k+2t 1 ) is clean Output: (b 0,..., b n 1 ) generated by Λ at distance E to (a 0,..., a n 1 )

20 Algorithm SequenceCleanUp Input: Λ(z) = z t + t 1 i=0 λ ix i where Λ(0) 0 Input: (a 0,..., a n 1 ), where n t + 1 Input: E, the maximum number of corrections to make Input: k, such that (a k, a k+2t 1 ) is clean Output: (b 0,..., b n 1 ) generated by Λ at distance E to (a 0,..., a n 1 ) 1 begin 2 (b 0,..., b n 1 ) (a 0,..., a n 1 ); e, j 0; 3 i k + 2t; 4 while i n 1 and e E do 5 if Λ does not satisfy (b i t+1,..., b i ) then 6 Fix b i using Λ(z) as a LFSR; e e + 1; 11 return (b 0,..., b n 1 ), e

21 Algorithm SequenceCleanUp Input: Λ(z) = z t + t 1 i=0 λ ix i where Λ(0) 0 Input: (a 0,..., a n 1 ), where n t + 1 Input: E, the maximum number of corrections to make Input: k, such that (a k, a k+2t 1 ) is clean Output: (b 0,..., b n 1 ) generated by Λ at distance E to (a 0,..., a n 1 ) 1 begin 2 (b 0,..., b n 1 ) (a 0,..., a n 1 ); e, j 0; 3 i k + 2t; 4 while i n 1 and e E do 5 if Λ does not satisfy (b i t+1,..., b i ) then 6 Fix b i using Λ(z) as a LFSR; e e + 1; 7 i k 1; 8 while i 0 and e E do 9 if Λ does not satisfy (b i,..., b i+t 1 ) then 10 Fix b i using z t Λ(1/z) as a LFSR; e e + 1; 11 return (b 0,..., b n 1 ), e

22 Algorithm SequenceCleanUp Input: Λ(z) = z t + t 1 i=0 λ ix i where Λ(0) 0 Input: (a 0,..., a n 1 ), where n t + 1 Input: E, the maximum number of corrections to make Input: k, such that (a k, a k+2t 1 ) is clean Output: (b 0,..., b n 1 ) generated by Λ at distance E to (a 0,..., a n 1 ) 1 begin 2 (b 0,..., b n 1 ) (a 0,..., a n 1 ); e, j 0; 3 i k + 2t; 4 while i n 1 and e E do 5 if Λ does not satisfy (b i t+1,..., b i ) then 6 Fix b i using Λ(z) as a LFSR; e e + 1; 7 i k 1; 8 while i 0 and e E do 9 if Λ does not satisfy (b i,..., b i+t 1 ) then 10 Fix b i using z t Λ(1/z) as a LFSR; e e + 1; 11 return (b 0,..., b n 1 ), e

23 Finding a clean segment: case E = 1 only one error (a 0,..., a k 2, b k 1 a k 1, a k, a k+1, a 2t 1 ) will be identified by the majority vote (2-to-1 majority).

24 Finding a clean segment: case E 2 Multiple errors on one segment can still be generated by Λ(z) deceptive segments: not good for SequenceCleanUp Example E = 3: (0, 1, 0, 2, 0, 4, 0, 8,... ) Λ(z) = z 2 2

25 Finding a clean segment: case E 2 Multiple errors on one segment can still be generated by Λ(z) deceptive segments: not good for SequenceCleanUp Example E = 3: (0, 1, 0, 2, 0, 4, 0, 8,... ) Λ(z) = z 2 2 (1, 1, 2, 2, 4, 4, 0, 8, 0, 16, 0, 32,... )

26 Finding a clean segment: case E 2 Multiple errors on one segment can still be generated by Λ(z) deceptive segments: not good for SequenceCleanUp Example E = 3: (0, 1, 0, 2, 0, 4, 0, 8,... ) Λ(z) = z 2 2 ( 1, 1, 2, 2, 4, 4, 0, 8, 0, 16, 0, 32,... ) }{{}}{{}}{{} z 2 2 z 2 +2z 2 z 2 2

27 Finding a clean segment: case E 2 Multiple errors on one segment can still be generated by Λ(z) deceptive segments: not good for SequenceCleanUp Example E = 3: (0, 1, 0, 2, 0, 4, 0, 8,... ) Λ(z) = z 2 2 ( 1, 1, 2, 2, 4, 4, 0, 8, 0, 16, 0, 32,... ) }{{}}{{}}{{} z 2 2 z 2 +2z 2 z 2 2 (1, 1, 2, 2) is deceptive. Applying SequenceCleanUp with this clean segment produces (1, 1, 2, 2, 4, 4, 8, 8, 16, 16, 32, 32, 64,... )

28 Finding a clean segment: case E 2 Multiple errors on one segment can still be generated by Λ(z) deceptive segments: not good for SequenceCleanUp Example E = 3: (0, 1, 0, 2, 0, 4, 0, 8,... ) Λ(z) = z 2 2 ( 1, 1, 2, 2, 4, 4, 0, 8, 0, 16, 0, 32,... ) }{{}}{{}}{{} z 2 2 z 2 +2z 2 z 2 2 (1, 1, 2, 2) is deceptive. Applying SequenceCleanUp with this clean segment produces (1, 1, 2, 2, 4, 4, 8, 8, 16, 16, 32, 32, 64,... ) E > 3? contradiction. Try (0, 16, 0, 32) as a clean segment instead.

29 Success of the sequence clean-up Theorem If n t(2e + 1), then a deceptive segment will necessarily be exposed by a failure of the condition e E in algorithm SequenceCleanUp.

30 Success of the sequence clean-up Theorem If n t(2e + 1), then a deceptive segment will necessarily be exposed by a failure of the condition e E in algorithm SequenceCleanUp. Corollary n 2t(2E + 1) is a necessary and sufficient condition for unique decoding of Λ and the corresponding sequence. Remark Also works with an upper bound t T on deg Λ.

31 List decoding for n 2t(E + 1) 2t E=2 n=2t(e+1) Λ Λ Λ 1 2 3

32 List decoding for n 2t(E + 1) 2t E=2 n=2t(e+1) Λ Λ Λ Input: (a 0,..., a n 1 ) + ε, where n = 2t(E + 1), weight(ε) E, and (a 0,..., a n 1 ) minimally generated by Λ of degree t, where Λ(0) 0. Output: (Λ i (z), s i = (a (i) 0,..., a(i) n 1 )) i a list of E candidates 1 begin 2 Run BMA on E + 1 segments of 2t entries and record Λ i (z) on each segment;

33 List decoding for n 2t(E + 1) 2t E=2 n=2t(e+1) Λ Λ Λ Input: (a 0,..., a n 1 ) + ε, where n = 2t(E + 1), weight(ε) E, and (a 0,..., a n 1 ) minimally generated by Λ of degree t, where Λ(0) 0. Output: (Λ i (z), s i = (a (i) 0,..., a(i) n 1 )) i a list of E candidates 1 begin 2 Run BMA on E + 1 segments of 2t entries and record Λ i (z) on each segment; 3 foreach Λ i (z) do 4 Use a clean segment to clean-up the sequence; 5 Withdraw Λ i if no clean segment can be found. 6 return the list (Λ i (z), (a (i) 0,..., a(i) n 1 )) i;

34 Properties The list contains the right solution (Λ, (a 0,..., a n 1 ))

35 Properties The list contains the right solution (Λ, (a 0,..., a n 1 )) n 2t(E + 1) is the tightest bound to enable syndrome decoding (BMA on a clean sequence of length 2t). Example n = 2t(E + 1) 1 and ε = (0,..., 0, 1, 0,..., 0, 1..., 1, 0,..., 0). }{{}}{{}}{{} 2t 1 2t 1 2t 1 Then (a 0,..., a n 1 ) + ε has no length 2t clean segment.

36 Outline Berlekamp/Massey algorithm with errors Bounds on the decoding capacity Decoding algorithms Sparse Polynomial Interpolation with errors Relations to Reed-Solomon decoding

37 Sparse Polynomial Interpolation x F f (x) Problem f = t i=1 c ix e i Recover a t-sparse polynomial f given a black-box computing evaluations of it.

38 Sparse Polynomial Interpolation x F f (x) Problem f = t i=1 c ix e i Recover a t-sparse polynomial f given a black-box computing evaluations of it. Ben-Or/Tiwari 1988: Let a i = f (p i ) for p a field element, and let Λ(λ) = t i=1 (z pe i ). Then Λ(λ) is the minimal generator of (a 0, a 1,... ). only need 2t entries to find Λ(λ) (using BMA)

39 Sparse Polynomial Interpolation x F f (x)+ε Problem f = t i=1 c ix e i Recover a t-sparse polynomial f given a black-box computing evaluations of it. Ben-Or/Tiwari 1988: Let a i = f (p i ) for p a field element, and let Λ(λ) = t i=1 (z pe i ). Then Λ(λ) is the minimal generator of (a 0, a 1,... ). only need 2t entries to find Λ(λ) (using BMA) only need 2T(2E + 1) with e E errors and t T.

40 Ben-Or & Tiwari s Algorithm Input: (a 0,..., a 2t 1 ) where a i = f (p i ) Input: t, the numvber of (non-zero) terms of f (x) = t j=1 c jx e j Output: f (x) 1 begin 2 Run BMA on (a 0,..., a 2t 1 ) to find Λ(z) 3 Find roots of Λ(z) (polynomial factorization) 4 Recover e j by repeated division (by p) 5 Recover c j by solving the transposed Vandermonde system (p 0 ) e 1 (p 0 ) e 2... (p 0 ) et c 1 (p 1 ) e 1 (p 1 ) e 2... (p 1 ) et c = (p t ) e 1 (p t ) e 2... (p t ) et c t a 0 a 1. a t 1

41 Outline Berlekamp/Massey algorithm with errors Bounds on the decoding capacity Decoding algorithms Sparse Polynomial Interpolation with errors Relations to Reed-Solomon decoding

42 Blahut s theorem Theorem (Blahut) The D.F.T of a vector of weight t has linear complexity at most t DFT ω (v) Vandemonde(ω 0, ω 1, ω 2,... )v Eval ω 0,ω 1,ω 2,...(v)

43 Blahut s theorem Theorem (Blahut) The D.F.T of a vector of weight t has linear complexity at most t DFT ω (v) Vandemonde(ω 0, ω 1, ω 2,... )v Eval ω 0,ω 1,ω 2,...(v) Univariate Ben-Or & Tiwari as a corollary

44 Blahut s theorem Theorem (Blahut) The D.F.T of a vector of weight t has linear complexity at most t DFT ω (v) Vandemonde(ω 0, ω 1, ω 2,... )v Eval ω 0,ω 1,ω 2,...(v) Univariate Ben-Or & Tiwari as a corollary Reed-Solomon codes: evaluation of a sparse error BMA

45 Reed-Solomon codes as Evaluation codes C = {(f (ω 1 ),..., f (ω n )) deg f < k} Evaluation f m=f(x), deg f < k 0 c = Eval(f), c = f(w i i ) error ε g g = f + Interp ( ε) Interpolation y = c + ε g

46 Reed-Solomon codes as Evaluation codes C = {(f (ω 1 ),..., f (ω n )) deg f < k} Evaluation f m=f(x), deg f < k 0 c = Eval(f), c i = f(w i ) error ε g g = f + Interp ( ε) BMA Interpolation y = c + ε f

47 Sparse interpolation with errors Find f from (f (w 1 ),..., f (w n )) + ε Interpolation error ε ε 0 c = Interp ( ε ) sparse polynomial f g g = Eval (f) + ε Evaluation y = c + f f

48 Sparse interpolation with errors Find f from (f (w 1 ),..., f (w n )) + ε Interpolation error ε ε 0 c = Interp ( ε ) sparse polynomial f g g = Eval (f) + ε Evaluation BMA y = c + f f

49 Same problems? Interchanging Evaluation and Interpolation Let V ω = Vandermonde(ω, ω 2,..., ω n ). Then (V ω ) 1 = 1 n V ω 1 Given g, find f, t-sparse and an error ε such that g = V ω f + ε V ω 1g = nf + V ω 1ɛ

50 Same problems? Interchanging Evaluation and Interpolation Let V ω = Vandermonde(ω, ω 2,..., ω n ). Then (V ω ) 1 = 1 n V ω 1 Given g, find f, t-sparse and an error ε such that g = V ω f + ε V ω 1g = nf }{{} + V ω 1ɛ }{{} weight t error RS code word Reed-Solomon decoding: unique solution provided ε has 2t consecutive trailing 0 s clean segment of length 2t n 2t(E + 1)

51 Same problems? Interchanging Evaluation and Interpolation Let V ω = Vandermonde(ω, ω 2,..., ω n ). Then (V ω ) 1 = 1 n V ω 1 Given g, find f, t-sparse and an error ε such that g = V ω f + ε V ω 1g = nf }{{} + V ω 1ɛ }{{} weight t error RS code word Reed-Solomon decoding: unique solution provided ε has 2t consecutive trailing 0 s clean segment of length 2t n 2t(E + 1) BUT: location of the syndrome, is a priori unknown no uniqueness

52 Applications and Perspectives Sparse interpolation with noise and outliers [Giesbrecht, Labahn &Lee 06] [Kaltofen, Lee, Yang 11]: Termination criteria for BMA: Exact singularity illconditionnedness Now combined with outliers

53 Applications and Perspectives Sparse interpolation with noise and outliers [Giesbrecht, Labahn &Lee 06] [Kaltofen, Lee, Yang 11]: Termination criteria for BMA: Perspectives: Exact singularity illconditionnedness Now combined with outliers surprising impact of noise on the sparsity: does not degenerate to dense

54 Applications and Perspectives Sparse interpolation with noise and outliers [Giesbrecht, Labahn &Lee 06] [Kaltofen, Lee, Yang 11]: Termination criteria for BMA: Perspectives: Exact singularity illconditionnedness Now combined with outliers surprising impact of noise on the sparsity: does not degenerate to dense Sparse rational function reconstruction with errors: dense case: Berlekamp/Welsh decoding and Padé approximant fit well together. sparse case?

55 Applications and Perspectives Sparse interpolation with noise and outliers [Giesbrecht, Labahn &Lee 06] [Kaltofen, Lee, Yang 11]: Termination criteria for BMA: Perspectives: Exact singularity illconditionnedness Now combined with outliers surprising impact of noise on the sparsity: does not degenerate to dense Sparse rational function reconstruction with errors: dense case: Berlekamp/Welsh decoding and Padé approximant fit well together. sparse case? application to k-error linear complexity (symmetric crypto)...