# Rank Bounds for Design Matrices and Applications ```Rank Bounds for Design Matrices
and Applications
Shubhangi Saraf
Rutgers University
Based on joint works with
Albert Ai, Zeev Dvir, Avi Wigderson
Sylvester-Gallai Theorem (1893)
v
v
v
v
Suppose that
every line
through two
points passes
through a
third
Sylvester Gallai Theorem
v
v
v
v
Suppose that
every line
through two
points passes
through a
third
Proof of Sylvester-Gallai:
• By contradiction. If possible, for every pair of points, the line
through them contains a third.
• Consider the point-line pair with the smallest distance.
P
m
Q
ℓ
dist(Q, m) &lt; dist(P, ℓ)
• Several extensions and variations studied
– Complexes, other fields, colorful, quantitative, high-dimensional
• Several recent connections to complexity theory
– Structure of arithmetic circuits
– Locally Correctable Codes
• BDWY:
–
–
–
–
Connections of Incidence theorems to rank bounds for design matrices
Lower bounds on the rank of design matrices
Strong quantitative bounds for incidence theorems
2-query LCCs over the Reals do not exist
• This work: builds upon their approach
– Improved and optimal rank bounds
– Improved and often optimal incidence results
– Stable incidence thms
• stable LCCs over R do not exist
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Points in Complex space
Kelly’s Theorem:
Hesse Configuration
For every pair of points in 𝐂 𝑑 ,
the line through them contains
a third, then all points
contained in a complex plane
[Elkies, Pretorius, Swanpoel 2006]:
First elementary proof
This work:
New proof using basic
linear algebra
𝛿𝑛
Quantitative SG
For every point there are at
least 𝜹𝒏 points s.t there is a
third point on the line
1
BDWY: dimension ≤ 𝑂(𝛿 2 )
This work:
dimension ≤ 𝑶
𝟏
𝜹
vi
Stable Sylvester-Gallai Theorem
v
v
v
v
Suppose that
for every two
points there
is a third that
is 𝜖 -collinear
with them
Stable Sylvester Gallai Theorem
v
v
v
v
Suppose that
for every two
points there
is a third that
is 𝜖 -collinear
with them
Other extensions
• High dimensional Sylvester-Gallai Theorem
• Colorful Sylvester-Gallai Theorem
• Average Sylvester-Gallai Theorem
• Generalization of Freiman’s Lemma
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Design Matrices
An m x n matrix is a
(q,k,t)-design matrix if:
1. Each row has at most q
non-zeros
2. Each column has at
least k non-zeros
3. The supports of every
two columns intersect
in at most t rows
n
&middot;q
&cedil;k
m
&middot;t
An example
(q,k,t)-design matrix
q=3
k=5
t=2
Main Theorem: Rank Bound
Thm: Let A be an m x n complex
(q,k,t)-design matrix then:
𝒏𝒕𝒒𝟐
𝒓𝒂𝒏𝒌 ≥ 𝒏 −
𝒌
Holds for any field of char=0 (or very large positive char)
Not true over fields of small characteristic!
Earlier Bounds (BDWY): 𝑟𝑎𝑛𝑘 ≥ 𝑛 −
𝑛𝑡𝑞 2
2𝑘
Rank Bound: no dependence on q
Thm: Let A be an m x n complex
(q,k,t)-design matrix then:
𝟔𝒏𝟑 𝒕𝟑
𝒓𝒂𝒏𝒌 ≥ 𝒏 −
𝒌𝟑
Square Matrices
Thm: Let A be an n x n complex
≈ 𝒏, ≈ 𝒏, ≈ 𝟏 -design matrix then:
𝒓𝒂𝒏𝒌 ≥ 𝛀(𝒏)
• Any matrix over the Reals/complex numbers with same zero-nonzero
pattern as incidence matrix of the projective plane has high rank
– Not true over small fields!
• Rigidity?
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Rank Bounds to Incidence Theorems
• Given 𝑣1 , 𝑣2 , … , 𝑣𝑛 ∈ 𝐂 𝑑
• For every collinear triple 𝑣𝑖 , 𝑣𝑗 , 𝑣𝑘 , ∃ 𝛼𝑖 , 𝛼𝑗 , 𝛼𝑘 so that 𝛼𝑖 𝑣𝑖 + 𝛼𝑗 𝑣𝑗
+ 𝛼𝑘 𝑣𝑘 = 0
• Construct 𝑛 &times; 𝑑 matrix V s.t 𝑖 𝑡ℎ row is 𝑣𝑖
• Construct 𝑚 &times; 𝑛 matrix 𝑨 s.t for each collinear triple 𝑣𝑖 , 𝑣𝑗 , 𝑣𝑘
there is a row with 𝛼𝑖 , 𝛼𝑗 , 𝛼𝑘 in positions 𝑖, 𝑗, 𝑘 resp.
• 𝑨⋅𝑽=𝟎
Rank Bounds to Incidence Theorems
• 𝐴⋅𝑉 =0
• Want: Upper bound on rank of V
• How?: Lower bound on rank of A
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Proof
Easy case: All entries are either zero or one
m
n
n
n
=n
At
A
Off-diagonals
&middot;t
m
Diagonal entries &cedil; k
“diagonal-dominant matrix”
General Case: Matrix scaling
Idea (BDWY) : reduce to easy case using matrixscaling:
c c …
c
1
Replace Aij with ri&cent;cj&cent;Aij
ri, cj positive reals
Same rank, support.
Has ‘balanced’ coefficients:
r1
r2
.
.
.
.
.
.
rm
2
n
Matrix scaling theorem
Sinkhorn (1964) / Rothblum and Schneider (1989)
Thm: Let A be a real m x n matrix with nonnegative entries. Suppose every zero minor of A
of size a x b satisfies
a
b
+ &middot; 1
m n
Then for every &sup2; there exists a scaling of A with
row sums 1 &plusmn; &sup2; and column sums (m/n) &plusmn; &sup2;
Can be applied also to squares of entries!
Scaling + design →
perturbed identity matrix
• Let A be an 𝑚 &times; 𝑛 scaled (𝑞, 𝑘, 𝑡)design matrix.
𝑚
(Column norms = , row norms = 1)
𝑛
• Let 𝑀 = 𝐴∗ 𝐴
•
𝑀𝑖𝑖 =
𝑚
𝑛
• 𝑀𝑖𝑗 ?
• BDWY:
• This work:
Mij ≤ 𝑂 𝑡
𝑖≠𝑗
2
𝑀𝑖𝑗
≤ 𝑡𝑚 1 −
1
𝑞
Bounding the rank of perturbed
identity matrices
M Hermitian matrix, 𝑀𝑖𝑖 ≥ 𝐿
𝑟𝑎𝑛𝑘 𝑀 ≥
𝑛𝐿
𝑛𝐿2 +
2
𝑖≠𝑗
2
𝑀𝑖𝑗
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Stable Sylvester-Gallai Theorem
v
v
v
v
Suppose that
for every two
points there
is a third that
is 𝜖 -collinear
with them
Stable Sylvester Gallai Theorem
v
v
v
v
Suppose that
for every two
points there
is a third that
is 𝜖 -collinear
with them
Not true in general ..
∃𝒏 points in ≈ 𝒏 dimensional space s.t for
every two points there exists a third point that
is
𝝐-collinear with them
Bounded Distances
• Set of points is B-balanced if all distances are
between 1 and B
• triple 𝑣𝑖 , 𝑣𝑗 , 𝑣𝑘 is 𝜖-collinear
∃ 𝛼𝑖 , 𝛼𝑗 , 𝛼𝑘 so that
𝛼𝑖 𝑣𝑖 + 𝛼𝑗 𝑣𝑗 + 𝛼𝑘 𝑣𝑘 ≤ 𝜖
and
1
4B
≤ 𝛼1 , 𝛼2 , 𝛼3 ≤ 1
Theorem
Let 𝑣1 , 𝑣2 , … , 𝑣𝑛 be a set of B-balanced points
in 𝐶 𝑑 so that
for each 𝑣𝑖 , 𝑣𝑗 there is a point 𝑣𝑘 such that
the triple is 𝜖- collinear.
Then
𝐷𝑖𝑚𝜖′ (𝑣1 , 𝑣2 , … , 𝑣𝑛 ) ≤ 𝑂 𝐵6
Incidence theorems to design matrices
• Given B-balanced 𝑣1 , 𝑣2 , … , 𝑣𝑛 ∈ 𝐶 𝑑
• For every almost collinear triple 𝑣𝑖 , 𝑣𝑗 , 𝑣𝑘 , ∃ 𝛼𝑖 , 𝛼𝑗 , 𝛼𝑘 &gt;
|𝛼𝑖 𝑣𝑖 + 𝛼𝑗 𝑣𝑗 + 𝛼𝑘 𝑣𝑘 | ≤ 𝜖
1
4𝐵
so that
• Construct 𝑛 &times; 𝑑 matrix V s.t 𝑖 𝑡ℎ row is 𝑣𝑖
• Construct 𝑚 &times; 𝑛 matrix 𝑨 s.t for each almost collinear triple
𝑣𝑖 , 𝑣𝑗 , 𝑣𝑘 there is a row with 𝛼𝑖 , 𝛼𝑗 , 𝛼𝑘 in positions 𝑖, 𝑗, 𝑘 resp.
• 𝑨 ⋅ 𝑽 = 𝑬 (Each row has small norm)
proof
• 𝑨⋅𝑽=𝑬
• Want to show rows of V are close to low dim space
• Suffices to show columns are close to low dim space
• Columns are close to the span of singular vectors of A
with small singular value
• Structure of A implies A has few small singular values
(Hoffman-Wielandt Inequality)
The Plan
• Extensions of the SG Theorem
• Improved rank bounds for design matrices
• From rank bounds to incidence theorems
• Proof of rank bound
• Stable Sylvester-Gallai Theorems
– Applications to LCCs
Correcting from Errors
Message
Encoding
Correction
≤ 𝜹fraction
Corrupted Encoding
Local Correction &amp; Decoding
Message
Encoding
Local
Correction
≤ 𝜹fraction
Corrupted Encoding
Stable Codes over the Reals
• Linear Codes
𝐸: 𝐑𝑘 → 𝐑𝑛
• Corruptions:
– arbitrarily corrupt 𝛿𝑛 locations
– small ℓ∞ perturbations on rest of the coordinates
• Recover message up to small perturbations
• Widely studied in the compressed sensing
literature
Our Results
Constant query stable LCCs over the Reals do not exist.
(Was not known for 2-query LCCs)
There are no constant query LCCs over the Reals
with decoding using bounded coefficients
Thanks!
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