Lower bounds for small depth arithmetic circuits Chandan Saha Joint work with Neeraj Kayal (MSRI)...

Lower bounds for small depth arithmetic circuits

Chandan Saha

Joint work with Neeraj Kayal (MSRI) Nutan Limaye (IITB)

Srikanth Srinivasan (IITB)

Arithmetic Circuit: A model of computation

+

x x x x

+ + + +

x x x x

….

…..

x1 x2 xn-1 xn

f(x1, x2, …, xn) --> multivariate polynomial in x1, …, xn

x

g h

gh

+

g h

g+h

Product gate

Sum gate

There are `field constants’ on the wires

Arithmetic Circuit: A model of computation

+

x x x x

+ + + +

x x x x

….

…..

x1 x2 xn-1 xn

f(x1, x2, …, xn)

Depth = 4

Arithmetic Circuit: A model of computation

+

x x x x

+ + + +

x x x x

….

…..

x1 x2 xn-1 xn

f(x1, x2, …, xn)

Size = no. of gates and wires

The lower bound question

Is there an explicit family of n-variate, poly(n) degree polynomials fn that requires…

…super-polynomial in n circuit size ?

The lower bound question

Is there an explicit family of n-variate, poly(n) degree polynomials fn that requires…

…super-polynomial in n circuit size ?

Note : A random polynomial has super-poly(n) circuit size

The Permanent – an explicit family

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

The Permanent – an explicit family

• Degree of Permn is low. i.e. bounded by poly(n)

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

The Permanent – an explicit family

• Degree of Permn is low.

• Coefficient of any given monomial can be found efficiently. …given a monomial, there’s a poly-time algorithm to determine the coefficient of the monomial.

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

The Permanent – an explicit family

• Degree of Permn is low.

• Coefficient of any given monomial can be found efficiently.

These two properties characterize explicitness

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

The Permanent – an explicit family

• Degree of Permn is low.

• Coefficient of any given monomial can be found efficiently.

Define class VNP

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

The Permanent – an explicit family

• Degree of Permn is low.

• Coefficient of any given monomial can be found efficiently.

Define class VNP

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

Class VP: Contains families of low degree polynomials fn that can be computed by poly(n)-size circuits.

The Permanent – an explicit family

• Degree of Permn is low.

• Coefficient of any given monomial can be found efficiently.

Permn = ∑ ∏ xi σ(i)σ є Sn i є [n]

VP vs VNP: Does Permn family require super-poly(n) size circuits?

A strategy for proving arithmetic circuit lower bound

Step 1: Depth reduction

Step 2: Lower bound for small depth circuits

A strategy for proving arithmetic circuit lower bound

Step 1: Depth reduction

Step 2: Lower bound for small depth circuits

Notations and Terminologies

Notations: n = no. of variables in fn

d = degree bound on fn = nO(1)

Homogeneous polynomial: A polynomial is homogeneous if all its monomials have the same degree (say, d).

Homogeneous circuits: A circuit is homogeneous if every gate outputs/computes a homogeneous polynomial.

Multilinear polynomial: In every monomial, degree of every variable is at most 1.

Reduction to depth ≈ log d

Valiant, Skyum, Berkowitz, Rackoff (1983). Homogeneous, degree d, fn computed by poly(n) circuit

fn computed by homogeneous poly(n) circuit of depth O(log d)

arbitrary depth≈ log d

poly(n) poly(n)

Reduction to depth 4

Agrawal, Vinay (2008); Koiran (2010); Tavenas (2013).

Homogeneous, degree d, fn computed by poly(n) circuit

fn computed by homogeneous depth 4 circuit of size nO(√d)

≈ log d4

nO(√d) poly(n)

Reduction to depth 4

Agrawal, Vinay (2008); Koiran (2010); Tavenas (2013).

Homogeneous, degree d, fn computed by poly(n) circuit

fn computed by homogeneous depth 4 circuit of size nO(√d)

≈ log d4

nO(√d) poly(n)

… fn can have nO(d) monomials !

A depth 4 circuit

+

x x x x

+ + + +

x x x x

….

…..

x1 x2 xn-1 xn

∑

∏

∑

∏

A depth 4 circuit

+

x x x x

+ + + +

x x x x

….

…..

x1 x2 xn-1 xn

∑ ∏ Qiji j

sum of monomialsQij

Reduction to depth 3

Gupta, Kamath, Kayal, Saptharishi (2013); Tavenas (2013).

Homogeneous, degree d, fn computed by poly(n) circuit

fn computed by depth 3 circuit of size nO(√d)

3

nO(√d) nO(√d)

4

Reduction to depth 3

Gupta, Kamath, Kayal, Saptharishi (2013); Tavenas (2013).

Homogeneous, degree d, fn computed by poly(n) circuit

fn computed by depth 3 circuit of size nO(√d)

3

nO(√d) nO(√d)

4

not homogeneous!

A depth 3 circuit

+

x x x x

+ + + +….

x1 x2 xn-1 xn

∑ ∏ liji j

linear polynomiallij

bottom fanin

Implication of the depth reductions

Let fn be an explicit family of polynomials.

if fn takes nω(√d) size homogeneous

if fn takes nω(√d) size

VP ≠ VNP or

4

3

A strategy for proving arithmetic circuit lower bound

Step 1: Depth reduction

Step 2: Lower bound for small depth circuits

Lower bound for homogeneous depth 4

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

…any homogeneous depth-4 circuit computing fn has size nΩ(√d)

size = nΩ(√d)

4

fn

Lower bound for homogeneous depth 4

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

…any homogeneous depth-4 circuit computing fn has size nΩ(√d)

size = nΩ(√d)

4

fn

fn = i

∑ ∏ Qij

… has size nΩ(√d)

j

sum of monomials

Lower bound for homogeneous depth 4

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

…any homogeneous depth-4 circuit computing fn has size nΩ(√d)

size = nΩ(√d)

4

fn

…joint work with Kayal, Limaye , Srinivasan

Lower bound for homogeneous depth 4

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

…any homogeneous depth-4 circuit computing fn has size nΩ(√d)

size = nΩ(√d)

4

fn

…the technique appears to be using homogeneity crucially

Lower bound for depth 3

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

any depth-3 circuit (bottom fanin ≤ √d) computing fn has size nΩ(√d)

size = nΩ(√d)

3

fn

Lower bound for depth 3

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

any depth-3 circuit (bottom fanin ≤ √d) computing fn has size nΩ(√d)

size = nΩ(√d)

3

fn

needn’t be homogeneous

Lower bound for depth 3

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

any depth-3 circuit (bottom fanin ≤ √d) computing fn has size nΩ(√d)

size = nΩ(√d)

3

fn Note: Even for bottom fanin ≤ √d, depth-3 circuits nω(√d) VP ≠ VNP

Lower bound for depth 3

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

any depth-3 circuit (bottom fanin ≤ t) computing fn has size nΩ(d/t)

size = nΩ(d/t)

3

fn

…joint work with Kayal

Lower bound for depth 3

Theorem: There is a family of homogeneous polynomials fn in VNP (with deg fn = d) such that…

any depth-3 circuit (bottom fanin ≤ t) computing fn has size nΩ(d/t)

size = nΩ(d/t)

3

fn

… answers a question by Shpilka & Wigderson (1999)

Proof ideas

Homogeneous depth-4 lower bound

Complexity measure• A measure is a function μ: F[x1, …, xn] -> R.

• We wish to find a measure μ such that

1. If C is a circuit (say, a depth 4 circuit) then μ(C) ≤ s. “small quantity” , where s = size(C)

2. For an “explicit” polynomial fn , μ(fn) ≥ “large quantity”

• Implication: If C = fn then s ≥ “large quantity”

“small quantity”

Upper bound

Lower bound

Some complexity measures Measure Model

Partial derivatives (Nisan & Wigderson) homogeneous depth-3 circuits

Evaluation dimension (Raz) multilinear formulas

Hessian (Mignon & Ressayre) determinantal complexity permanent

Jacobian (Agrawal et. al.) occur-k, depth-4 circuits

Incomplete list ?

Some complexity measures Measure Model

Partial derivatives (Nisan & Wigderson) homogeneous depth-3 circuits

Evaluation dimension (Raz) multilinear formulas

Hessian (Mignon & Ressayre) determinantal complexity permanent

Jacobian (Agrawal et. al.) occur-k, depth-4 circuits

Shifted partials (Kayal; Gupta et. al.) homog. depth-4 with low bottom fanin

Projected shifted partials homogeneous depth-4 circuits;

depth-3 circuits (with low bottom fanin)

Space of Partial Derivatives Notations:

∂=k f : Set of all kth order derivatives of f(x1, …, xn)

< S > : The vector space spanned by F-linear combinations of polynomials in S

Definition: PDk(f) = dim(< ∂=k f >)

Sub-additive property: PDk(f1 + f2) ≤ PDk(f1) + PDk(f2)

Space of Shifted Partials

Notation: x=ℓ = Set of all monomials of degree ℓ

Definition: SPk,ℓ (f) := dim (< x=ℓ . ∂=k f >)

Sub-additivity: SPk,ℓ (f1 + f2) ≤ SPk,ℓ (f1) + SPk,ℓ (f2)

Space of Shifted Partials

Notation: x=ℓ = Set of all monomials of degree ℓ

Definition: SPk,ℓ (f) := dim (< x=ℓ . ∂=k f >)

Sub-additivity: SPk,ℓ (f1 + f2) ≤ SPk,ℓ (f1) + SPk,ℓ (f2)

Why do we expect SP(C) to be small ?

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Qij = Sum of monomials

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Observation: ∂=k Qi1…Qim has “many roots” if k << m << n

… any common root of Qi1…Qim is also a common root of ∂=k Qi1…Qim

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Observation: Dimension of the variety of ∂=k Qi1…Qim is large if k << m << n

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Observation: Dimension of the variety of ∂=k Qi1…Qim is large if k << m << n

[Hilbert’s] Theorem (informal): If dimension of the variety of g is large then dim (< x=ℓ . g >) is small.

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Observation: Dimension of the variety of ∂=k Qi1…Qim is large if k << m << n

[Hilbert’s] Theorem (informal): If dimension of the variety of g is large then dim (< x=ℓ . g >) is small.

… so we expect SPk,ℓ (Qi1…Qim) to be a `small quantity’

Shifted partials – the intuition C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Observation: Dimension of the variety of ∂=k Qi1…Qim is large if k << m << n

[Hilbert’s] Theorem (informal): If dimension of the variety of g is large then dim (< x=ℓ . g >) is small.

… by subadditivity, SPk,ℓ (C) ≤ s . `small quantity’

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Qij = Sum of monomials of degree ≤ t(w.l.o.g m ≤ 2d/t )

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

degree ≤ k.t

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

at most ( ) termsmk

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

u . ∂=k Qi1…Qim = Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …X

degree = ℓ degree ≤ ℓ + k.t

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

u . ∂=k Qi1…Qim = Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …X

n + ℓ + ktn

mkSPk,ℓ

(Qi1…Qim) ≤ ( ) . ( )

Depth-4 with low bottom degree C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

u . ∂=k Qi1…Qim = Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …X

n + ℓ + ktn

mkSPk,ℓ

(C) ≤ s. ( ) . ( ) Upper bound

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm (homog. depth 4)

Qij = Sum of monomials (NO degree restriction)

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Idea: Reduce to the case of low bottom degree using

• Random restriction

• Multilinear projection

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Random restriction: Set every variable to zero independently at random with a certain probability.

…denoted naturally by a map σ

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Random restriction: Set every variable to zero independently at random with a certain probability.

…denoted naturally by a map σ

σ(C) = σ(Q11) σ(Q12)…σ(Q1m) + … + σ(Qs1) σ(Qs2)…σ(Qsm)

Obs: If a monomial u has many variables (high support) then σ(u) = 0 w.h.p

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Random restriction: Set every variable to zero independently at random with a certain probability.

…denoted naturally by a map σ

σ(C) = σ(Q11) σ(Q12)…σ(Q1m) + … + σ(Qs1) σ(Qs2)…σ(Qsm)

w.l.o.g σ(Qij) = sum of ‘low support’ monomials

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Random restriction: Set every variable to zero independently at random with a certain probability.

Homogeneous depth 4 homogenous depth 4 with low bottom support

… w.l.o.g assume that C has low bottom support

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Projection map: π (g) = sum of the multilinear monomials in g

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Projection map: π (g) = sum of the multilinear monomials in g

Observation: π (sum of ‘low support’ monomials) = sum of ‘low degree’ monomials

Reduction to low bottom degreeC = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Projection map: π (g) = sum of the multilinear monomials in g

Observation:

π (Qij ) = sum of ‘low degree’ monomials

Projected Shifted Partials

PSPk,ℓ (f) := dim (π (x=ℓ. ∂=k f) )(obeys subadditivity)

Projected Shifted Partials

PSPk,ℓ (f) := dim (π (x=ℓ. ∂=k f) )(obeys subadditivity)

multilinear shifts only!

Projected Shifted Partials

PSPk,ℓ (f) := dim (π (x=ℓ. ∂=k f) )(obeys subadditivity)

multilinear derivatives!

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

support of every monomial bounded by t

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Every variable in every monomial has degree 2 or less

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Every monomial has a variable with degree 3 or more

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Qi1Qi2…Qim = Q’i1Q’i2…Q’im +

Every monomial has a variable with degree 3 or more

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Qi1Qi2…Qim = Q’i1Q’i2…Q’im +

PSPk,ℓ (Qi1Qi2…Qim) ≤ PSPk,ℓ (Q’i1Q’i2…Q’im) + PSPk,ℓ( )

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Qi1Qi2…Qim = Q’i1Q’i2…Q’im +

PSPk,ℓ (Qi1Qi2…Qim) ≤ PSPk,ℓ (Q’i1Q’i2…Q’im) + PSPk,ℓ( )

0

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Qi1Qi2…Qim = Q’i1Q’i2…Q’im +

PSPk,ℓ (Qi1Qi2…Qim) ≤ PSPk,ℓ (Q’i1Q’i2…Q’im) + PSPk,ℓ( )

0

degree ≤ 2t

Depth-4 with low bottom support C = Q11Q12…Q1m + … + Qs1Qs2…Qsm

Qij = Q’ij +

Qi1Qi2…Qim = Q’i1Q’i2…Q’im +

PSPk,ℓ (Qi1Qi2…Qim) ≤ PSPk,ℓ (Q’i1Q’i2…Q’im)

Abusing notation: Call Q’ij as Qij

Depth-4 with low bottom support

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

degree ≤ 2kt

Depth-4 with low bottom support

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

u . ∂=k Qi1…Qim = u. Qi k+1 … Qim + u. Qi1 Qi k+2 … Qim +X

degree = ℓ degree ≤ 2kt

Depth-4 with low bottom support

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

π(u.∂=k Qi1…Qim) = π( Qi k+1 … Qim) + π( Qi1 Qi k+2 … Qim) +X

multilinear, degree ≤ ℓ + 2k.t

Depth-4 with low bottom support

∂=k Qi1…Qim = Qi1 Qi2…Q ik …Qim + Qi1 Qi2…Q ik Q i k+1…Qim + … X

. . . . ..

= Qi k+1 … Qim + Qi1 Qi k+2 … Qim + …

π(u.∂=k Qi1…Qim) = π( Qi k+1 … Qim) + π( Qi1 Qi k+2 … Qim) +X

Upper bound ℓ + 2kt

n mkSPk,ℓ

(C) ≤ s. ( ) . ( )

How large can PSP(f) be?• Trivially,

PSPk,ℓ (f) ≤ min ( ).( ) , ( ) nk

nℓ

n ℓ + d - k

How large can PSP(f) be?• Trivially,

PSPk,ℓ (f) ≤ min ( ).( ) , ( ) nk

nℓ

n ℓ + d - k

• Size of the set x=ℓ. ∂=k f ≤ ( ).( )

• Number of monomials in any polynomial in π (x=ℓ. ∂=k f) ≤ ( )

nk

nℓ

n ℓ + d - k

Let f be a multilinear polynomial

How large can PSP(f) be?• Trivially,

PSPk,ℓ (f) ≤ min ( ).( ) , ( )

• Best lower bound for s

s ≥

nk

nℓ

nℓ + d - k

min ( ).( ) , ( ) ( ).( ) m

kn

ℓ + 2kt

nk

nℓ

nℓ + d - k = nΩ(d/t)

After setting k and ℓ appropriately

How large can PSP(f) be?• Trivially,

PSPk,ℓ (f) ≤ min ( ).( ) , ( )

• Best lower bound for s

s ≥

• There’s an explicit f such that PSPk,ℓ (f) is close to the trivial upper bound. (lower bound)

nk

nℓ

nℓ + d - k

min ( ).( ) , ( ) ( ).( ) m

kn

ℓ + 2kt

nk

nℓ

nℓ + d - k = nΩ(d/t)

Depth-3 lower bound

Trading depth for homogeneity

Idea: Depth-3 with low bottom fanin

Homogeneous depth-4 with low bottom support

Size = sBottom fanin = t

3

fn

4 (homogeneous)

fn

Size = s . 2O(√d)

Bottom support = t

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

C = α1.(1 + l11)(1 + l12)…(1 + l1m) + …. + αs.(1 + ls1)(1 + ls2)…(1 + lsm)

linear formsfield constants

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

C = (1 + l11)(1 + l12)…(1 + l1m) + …. + (1 + ls1)(1 + ls2)…(1 + lsm)

Notation: [g]d = d-th homogeneous part of g

Easy observation: If C = f , which is homogeneous deg d polynomial, then [C]d = f.

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

C = (1 + l11)(1 + l12)…(1 + l1m) + …. + (1 + ls1)(1 + ls2)…(1 + lsm)

[C]d = [(1 + l11)(1 + l12)…(1 + l1m)]d +….+ [(1 + ls1)(1 + ls2)…(1 + lsm)]d

idea: transform these to homogeneous depth-4

Newton’s identities

• Ed (y1, y2, …, ym) := ∑ ∏ yj

• Pr (y1, y2, …, ym) := ∑ yjr

S in 2[m] |S| = d

j in S

(elementary symmetric polynomial of degree d)

j in [m]

(power symmetric polynomial of degree r)

Newton’s identities

• Ed (y1, y2, …, ym) := ∑ ∏ yj

• Pr (y1, y2, …, ym) := ∑ yjr

S in 2[m] |S| = d

j in S

j in [m]

Lemma: Ed (y) = ∑ βa ∏ Pr (y) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

e.g. 2y1y2 = (y1 + y2)2 – y12 – y2

2 = P1

2 – P2

field constant

Newton’s identities

• Ed (y1, y2, …, ym) := ∑ ∏ yj

• Pr (y1, y2, …, ym) := ∑ yjr

S in 2[m] |S| = d

j in S

j in [m]

Lemma: Ed (y) = ∑ βa ∏ Pr (y) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

Hardy-Ramanujan estimate:

The number of a = (a1, …, ad) such that ∑ r.ar = d is 2O(√d)

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

[(1 + li1)(1 + li2)…(1 + lim)]d = Ed ( li1 , … , lim )

= ∑ βa ∏ Pr ( li1 , … , lim ) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

2O(√d) summands

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

[(1 + li1)(1 + li2)…(1 + lim)]d = Ed ( li1 , … , lim )

= ∑ βa ∏ Pr ( li1 , … , lim ) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

2O(√d) summands

Suppose every lij has at most t variables, then…

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

[(1 + li1)(1 + li2)…(1 + lim)]d = Ed ( li1 , … , lim )

= ∑ βa ∏ Pr ( li1 , … , lim ) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

= ∑ βa ∏ Qi,a,r a = (a1, … , ad)∑ r . ar = d

r in [d]

every monomial has support ≤ t

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

[(1 + li1)(1 + li2)…(1 + lim)]d = Ed ( li1 , … , lim )

= ∑ βa ∏ Pr ( li1 , … , lim ) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

= ∑ βa ∏ Qi,a,r a = (a1, … , ad)∑ r . ar = d

r in [d]

[C]d = ∑ ∑ βa ∏ Qi,a,r a = (a1, … , ad)∑ r . ar = d

r in [d]i in [s]

Depth-3 to Depth-4

• Implicit in Shpilka & Wigderson ; Hrubes & Yehudayoff (2011)

[(1 + li1)(1 + li2)…(1 + lim)]d = Ed ( li1 , … , lim )

= ∑ βa ∏ Pr ( li1 , … , lim ) a = (a1, … , ad)∑ r . ar = d

r in [d]

ar

= ∑ βa ∏ Qi,a,r a = (a1, … , ad)∑ r . ar = d

r in [d]

[C]d = ∑ ∑ βa ∏ Qi,a,r a = (a1, … , ad)∑ r . ar = d

r in [d]i in [s]

Homogeneous depth-4 with low bottom support and size s.2Ω(√d)

An explicit family with high PSPk,ℓ

An explicit family of polynomials• Nisan-Wigderson family of polynomials:

NWr := ∑ ∏ xi, h(i)d2 h(z) in F [z],

deg(h) ≤ ri in [d]

identifying the elements of F with 1,2, … , d2d2

An explicit family of polynomials• Nisan-Wigderson family of polynomials:

NWr := ∑ ∏ xi, h(i)d2 h(z) in F [z],

deg(h) ≤ ri in [d]

`Disjointness’ property: Two monomials can share at most r ≈ d/3 variables.

= + + …

d

r r

d2(r+1) monomials

Projected Shifted Partials of NWr

• The set π (x=ℓ. ∂=k NWr) has ( ).( ) elements.

• Every polynomial in π (x=ℓ. ∂=k NWr) is multilinear & homogeneous of degree (ℓ + d – k).

nk

nℓ

Projected Shifted Partials of NWr

• The set π (x=ℓ. ∂=k NWr) has ( ).( ) elements.

• Every polynomial in π (x=ℓ. ∂=k NWr) is multilinear & homogeneous of degree (ℓ + d – k).

• PSPk,ℓ (NWr) = rank (M)

nk

nℓM := ( ).( ) rows

π (x=ℓ. ∂=k NWr)

(0/1)-matrix of coefficients

nℓ + d - k ( ) columns

nk

nℓ

Projected Shifted Partials of NWr

• Because of the `disjointness property’ of NWr , the columns of M are almost orthogonal.

• Hence, B := MT M is diagonally dominant.

• Observe, rank (M) ≥ rank (B) .

Projected Shifted Partials of NWr

• Because of the `disjointness property’ of NWr , the columns of M are almost orthogonal.

• Hence, B := MT M is diagonally dominant.

• Observe, rank (M) ≥ rank (B) .

Alon’s rank bound (for diagonally dominant matrix):

If B is a real symmetric matrix then

rank (B) ≥ Tr (B)2

Tr (B2)

Projected Shifted Partials of NWr

[Main lemma]: Using Alon’s bound and settings r , k and ℓ appropriately,

PSPk,ℓ (NWr) ≥ η. min ( ).( ) , ( )nk

nℓ

nℓ + d - k

small factor

An explicit family in VP• [Kumar-Saraf (2014)] : Showed the same lower bound using

the Iterated Matrix multiplication polynomial, which is in VP

An explicit family in VP• [Kumar-Saraf (2014)] : Showed the same lower bound using

the Iterated Matrix multiplication polynomial, which is in VP

VNP

Circuits (VP)

ABPs

Formulas

Depth-4

exponential separation

An explicit family in VP• [Kumar-Saraf (2014)] : Showed the same lower bound using

the Iterated Matrix multiplication polynomial, which is in VP

VNP

Circuits (VP)

ABPs

FormulasOpen: separation ?

…known in the multilinear setting[Dvir, Malod, Perifel, Yehudayoff (2012)]

An explicit family in VP• [Kumar-Saraf (2014)] : Showed the same lower bound using

the Iterated Matrix multiplication polynomial, which is in VP

VNP

Circuits (VP)

ABPs

Formulas

Open: separation ?

…improve nΩ(√d) to nω(√d)

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits (i.e. without the low bottom fanin restriction).

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits.

2. Prove a nΩ(√d) lower bound for homogeneous depth-5 circuits. [open problem in Nisan & Wigderson (1996)]

(2) (1)

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits.

2. Prove a nΩ(√d) lower bound for homogeneous depth-5 circuits.

3. Prove a nΩ(d) lower bound for multilinear depth-3 circuits. (current best is 2Ω(d) )

…interestingly, one can get this using PSP measure

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits.

2. Prove a nΩ(√d) lower bound for homogeneous depth-5 circuits.

3. Prove a nΩ(d) lower bound for multilinear depth-3 circuits.

4. A separation between homogeneous formulas and homogeneous depth-4 formulas.

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits.

2. Prove a nΩ(√d) lower bound for homogeneous depth-5 circuits.

3. Prove a nΩ(d) lower bound for multilinear depth-3 circuits.

4. A separation between homogeneous formulas and homogeneous depth-4 formulas.

5. A separation between homogeneous formulas and multilinear homogeneous formulas.

…exhibiting the power of non-multilinearity

Some other open questions

1. Prove a nΩ(√d) lower bound for general depth-3 circuits.

2. Prove a nΩ(√d) lower bound for homogeneous depth-5 circuits.

3. Prove a nΩ(d) lower bound for multilinear depth-3 circuits.

4. A separation between homogeneous formulas and homogeneous depth-4 formulas.

5. A separation between homogeneous formulas and multilinear homogeneous formulas.

Thanks!