1. expander codes and pseudorandom subspaces of R n James R. Lee University of Washington [joint with Venkatesan Guruswami (Washington) and Alexander Razborov (IAS/Steklov)]

2. random sections of the cross polytope Classical high-dimensional geometry [Kasin 77, Figiel-Lindenstrauss-Milman 77]: For a random subspace X µ R N with dim(X) = N/2, (e.g. choose X = span {v 1, …, v N/2 } where v i are i.i.d. on the unit sphere) In other words, every x 2 X has its L 2 mass very “spread” out: This holds not only for each v i, but every linear combination

3. random sections of the cross polytope Classical high-dimensional geometry [Kasin 77, Figiel-Lindenstrauss-Milman 77]: For a random subspace X µ R N with dim(X) = N/2, (e.g. choose X = span {v 1, …, v N/2 } where v i are i.i.d. on the unit sphere)

4. an existential crisis Geometric functional analysts face a dilemma we know well: Almost every subspace satisfies this property, but we can’t pinpoint even one. [Szarek, ICM 06 ; Milman, GAFA 01 ; Johnson-Schechtman, handbook 01 ] asked: Can we find an explicit subspace on which the L 1 and L 2 norms are equivalent? This is a prominent example of the (now ubiquitous) use of the probabilistic method in asymptotic convex geometry. Related questions about explicit, high-dim. constructions arose (concurrently) in CS: - explicit embeddings of L 2 into L 1 for nearest-neighbor search (Indyk) - explicit compressed sensing matrices M : R N  R n for n ¿ N (Devore) - explicit Johnson-Lindenstrauss (dimension reduction) transform (Ailon-Chazelle) Why do analytists / CSists care about explicit high-dimensional constructions?

5. distortion For a subspace X µ R N, we define the distortion of X by By Cauchy-Schwarz, we always have N 1/2 ¸  (X) ¸ 1. dim(X) =   (N) and  (X) · 1 + . [Fiegel-Lindenstrauss-Milman 77] Random construction: A random X µ R N satisfies: dim(X) = ( 1 -  )N and  (X) = O  ( 1 ). [Kasin 77] Let X = ker(first N/2 rows of Hadamard), then  (X) ¼ N 1/4. Example (Hadamard):

6. applications distortion dimension Nearest-neighbor search Compressive sensing Coding in characteristic zero, Geometric functional analysis View as an embedding: 1 +  distortion, small blowup in dimension O( 1 ) distortion,  (N) dimension Want a map A : R N  R n with n ¿ N, such that any r-sparse signal x 2 R N (vector with at most r non-zero entries) can be uniquely and efficiently recovered from Ax. Can uniquely and efficiently recover any r-sparse signal for r · N/  (ker(A)) 2. (Even tolerates additional “noise” in the “non-sparse” parts of the signal.) Relation to distortion: [Kashin-Temlyakov] (Milman believes impossible)

7. sensing and distortion Want a map A : R N  R n such that any r-sparse signal x 2 R N (vector with at most r non-zero entries) can be uniquely and efficiently recovered from Ax. Basis Pursuit: Given compressed signal y, minimize || x || 1 subject to Ax = y. (P1) Want to solve: Given compressed signal y, minimize || x || 0 subject to Ax = y. (P0) Highly non-convex optimization problem, NP-hard for general A. Can use linear programming! [KT07]: If y = Av and v has at most N/[2  (ker(A))] 2 non-zero coordinates, then (P0) and (P1) give the same answer. let’s prove this [Lots of work has been done here: Donoho et. al.; Candes-Tao-Romberg; etc.]

8. sensing and distortion [KT07]: If y = Av and v has at most N/[2  (ker(A))] 2 non-zero coordinates, then (P0) and (P1) give the same answer. For x 2 R N and S µ [N], let x S be x restricted to coordinates in S. If x 2 ker(A) and

9. previous results: explicit Sub-linear dimension: Rudin’60 (and later LLR’94) achieve dim(X) ¼ N 1/2 and  (X) · 3 (X = span {4-wise independent vectors}) Indyk’07 achieves dim(X) ¼ N/2 (log log N) 2 and  (X) = 1 +o( 1 ). Indyk’00 achieves dim(X) ¼ exp((log N) 1/2 ) and  (X) = 1 +o( 1 ). We construct an explicit subspace X µ R N with dim(X) = ( 1 -o( 1 ) ) N and Our result: In our constructions, X = ker(explicit sign matrix).

10. previous results: derandomization Partial derandomization: Let A k, N be a random k £ N sign matrix (entries are ± 1 i.i.d) Kashin’s technique shows that almost surely, (and dim(ker(A k, N )) ¸ N – k) Can reduce to O(N log 2 N) random bits [Indyk 00] Can reduce to O(N log N) random bits [Artstein-Milman 06] Can reduce to O(N) random bits [Lovett-Sodin 07] With N o(1) random bits, we get  (X) · polylog(N). Our result: With N  random bits for any , we get  (X) = O  ( 1 ). [Guruswami-L-Wigderson]

11. the expander code construction G = ([N], [n], E) - bipartite graph, d-right-regular and L µ R d a subspace. where x S 2 R |S| is x restricted to the coordinates in S µ [N] and  (j) is the neighborhood of j. N n À d j Resembles construction of Gallager, Tanner (L is the “inner” code). Following Tanner and Sipser-Spielman, we will show that if L is “good” and G is an “expander” then X(G,L) is even better (in some parameters). x1x1 x2x2 x3x3 xNxN

12. some quantitative matters Say that a subspace L µ R d is (t,  )-spread if every x 2 L satisfies If L is (  (d),  )-spread, then Conversely, if L has  (L) = O( 1 ), then L is (  (d),  ( 1 ))-spread. For a bipartite graph G = ([N],[n],E), the expansion profile of G is (This is expansion from left to right.)

13. spread-boosting theorem G = ([N], [n], E) - bipartite graph, d-right-regular and left degree · D. Setup: L µ R d a (t,  )-spread subspace. Conclusion: If X(G,L) is (T,  )-spread, then X(G,L) is How to apply: Assume D = O( 1 ) and  G (q) =  (q) 8 q 2 [N] (impossible to achieve) X(G,L) is (½, 1 )-spread ) (t,  )-spread ) (t 2,  2 )-spread … ) (  (N),  log t (N) )-spread )  (X(G,L)). ( 1 /  log t (N)

14. spread-boosting theorem G = ([N], [n], E) - bipartite graph, d-right-regular and left degree · D. Setup: L µ R d a (t,  )-spread subspace. Conclusion: If X(G,L) is (T,  )-spread, then X(G,L) is S S should “leak” L 2 mass outside (since L is spreading and G is an expander), unless most of the mass in S is concentrated on a small subset B (impossible by assumption) B

15. when L is random Let H be a (non-bipartite) d-regular graph with second eigenvalue = O(d 1/2 ). Let G be the edge-vertex incidence graph (an edge is connected to its endpoints) edges of H nodes of H Alon-Chung: Random subspace L µ R d is (  (d),  ( 1 ))-spread Letting d = N 1/4, the spread-boosting thm gives X(G,L) is (T,  )-spread ) X(G,L) is Takes O(log log N) steps to reach  (N)-sized sets ) poly(log N) distortion. (explicit constructions exist by Margulis, Lubotsky-Phillips-Sarnak)

16. explicit construction: ingredients for L Let A be any k £ d matrix whose columns a 1, …, a d 2 R k are unit vectors and such that for every i  j, | h a i, a j i | · . Kerdock codes (aka Mutually Unbiased Bases) [Kerdock’72, Cameron-Seidel’73] Spectral Lemma: Then ker(A) is (  (d 1/2 ),  ( 1 ) ) -spread subspaces of dimension ( 1 -  )d for every eps>0 +

17. boosting L with sum-product expanders Kerdock + Spectral Lemma gives (  (d 1/2 ),  ( 1 ) ) -spread subspaces of dimension ( 1 -  )d for every eps>0 Problem: If G=Ramanujan construction and L=Kerdock, the spread-boosting theorem gives nothing. (Ramanujan loses d 1/2 and Kerdock gains only d 1/2 ) Solution: Produce L’ = X(G,L) where L=Kerdock and G=sum-product expander Sum-product theorems [Bourgain-Katz-Tao, …] For A µ F p, with |A| · p 0.99 we have

18. boosting L with sum-product expanders Kerdock + Spectral Lemma gives (  (d 1/2 ),  ( 1 ) ) -spread subspaces of dimension ( 1 -  )d for every eps>0 Problem: If G=Ramanujan construction and L=Kerdock, the spread-boosting theorem gives nothing. (Ramanujan loses d 1/2 and Kerdock gains only d 1/2 ) Solution: Produce L’ = X(G,L) where L=Kerdock and G=sum-product expander Using [Barak-Impagliazzo-Wigderson/BKSSW] and the spread-boosting theorem, L’ is ( d 1/2+c,  ( 1 ) ) -spread for some c > 0.

19. boosting L with sum-product expanders Solution: Produce L’ = X(G,L) where L=Kerdock and G=sum-product expander Using [Barak-Impagliazzo-Wigderson/BKSSW] and the spread-boosting theorem, L’ is ( d 1/2+c,  ( 1 ) ) -spread for some c > 0. Now we can plug L’ into G=Ramanujan and get non-trivial boosting. (almost done…)

20. some open questions - Improve the current bounds: First attempt would be O( 1 ) distortion with sub-linear randomness. - Stronger pseudorandom properties: Restricted Isometry Property [T. Tao’s blog] Improve dependence on the co-dimension (important for compressed sensing) If dim(X) ¸ ( 1 -  )N, we get distortion dependence ( 1 /  O(log log N). - Breaking the diameter bound: Show that the kernel of a random { 0,1 } matrix with only 100 ones per row has small distortion. Or prove that sparse matrices cannot work. Could hope for. Find an explicit collection of unit vectors v 1, v 2, …, v N 2 R n with N À n so that every small enough sub-collection is “nearly orthogonal.”

21. some open questions - Refuting random subspaces with high distortion Give efficiently computable certificates for  (X) small or Restricted Isometry Property which exist almost surely for random X µ R N. - Linear time expander decoding? Are their recovery schemes that run faster than Basis Pursuit?