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On obtaining a polynomial as a projection of another polynomial. Neeraj Kayal Microsoft Research. A dream. Conjecture #1: The determinantal complexity of the permanent is superpolynomial Conjecture #2: The arithmetic complexity of matrix multiplication is - PowerPoint PPT Presentation
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On obtaining a polynomial as a projection of another
polynomial
Neeraj KayalMicrosoft Research
A dream
• Conjecture #1: The determinantal complexity of the permanent is superpolynomial
• Conjecture #2: The arithmetic complexity of matrix multiplication is
• Conjecture #3: The depth three complexity of the permanent is
Projections of polynomials
• Definition (Valiant): An n-variate polynomial is said to be a projection of an m-variate polynomial if
for some matrix A and vector . That is ,
where the ’s are affine functions. (Affine function : ) We will say that such a projection is invertible if is invertible.
Some families of polynomials
• Determinant:
• Permanent:
• Sum of Products:
Some families of polynomials• Trace of Matrix Multiplication
where X, Y and Z are matrices
• Sum of powers:
• Elementary Symmetric Polynomial:
][
dn, :POWni
dix
)(Trace:MMn ZYX
dSnS Si
ixYM||],[
dn, :S
The dream
• Conjecture #1: The determinantal complexity of the permanent is superpolynomial
• Conjecture #2: The arithmetic complexity of matrix multiplication is
• Conjecture #3: The depth three complexity of the permanent is
The dream
• Conjecture #1: If is a projection of then m is superpolynomial in n.
• Conjecture #2: The arithmetic complexity of matrix multiplication is
• Conjecture #3: The depth three complexity of the permanent is
The dream
• Conjecture #1: If is a projection of then m is superpolynomial in n.
• Conjecture #2: can be expressed as a projection of for m =
• Conjecture #3: The depth three complexity of the permanent is
The dream
• Conjecture #1: If is a projection of then m is superpolynomial in n.
• Conjecture #2: can be expressed as a projection of for m =
• Conjecture #3: If is a projection of then .
A computational problem
• POLY_PROJECTION: Given polynomials and , determine if is a projection of and if so find A and b such that
Some conventions
• We consider polynomials over , the field of complex numbers.
• Polynomials are encoded as arithmetic circuits (unless mentioned otherwise).
Unfortunately …
Theorem: POLY_PROJECTION is NP-complete.
A more modest ambition
• Given a polynomial and an integer n determine if is a projection of say .
• Given a polynomial and integers n,d determine if is a projection of .
A conjecture
Conjecture (Scott Aaronson): A random low rank projection of is indistinguishable from a truly random polynomial.
More precisely, if are random m-variate affine functions then is indistuinguishable from a random m-variate polynomial of degree n.
POLY_EQUIVALENCE: Given polynomials and , determine if for some invertible matrix .
Theorem (Agrawal-Saxena): POLY_EQUIVALENCE is at least as hard as graph isomorphism.
(Probably much harder than graph isomorphism.)
Polynomial Equivalence
• Given a polynomial and an integer n determine if is equivalent to .
• Given a polynomial and integers n,d determine if is equivalent to .
Lowering our sights further
Polynomial Equivalence Results
Theorem #1: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
Theorem #2: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
Invertible Projections
Theorem #3: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
Theorem #4: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
Invertible ProjectionsTheorem #3: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
Theorem #4: There is an efficient randomized algorithm that determines if a given polynomial is an invertible projection of .
… and so on for the other families of polynomials
PreliminariesFact: Given polynomials in randomized polynomial time we can determine a basis of the vector space
}0...:),...,,{(: 1121 mmm
m ffCV
Fact: Given an n-variate polynomial , in randomized polynomial time we can find an invertible matrix A such that has fewer than n variables, if such an exists.
Equivalence to Problem restatement: Given an n-variate polynomial of degree d, determine if there exist n linearly independent affine functions such that
dn
ddxf ...)( 21
Introducing the Hessian
Definition: The hessian of an n-variate polynomial g is the following matrix:
ggg
gggggg
xH
nnnn
n
n
f
21
22
212
1212
1
:)(
Property: If then
(here g is a shorthand for the second order derivative )
Using the HessianProperty: If then
In particular and
Fact:
2
22
21
00
0000
)1()(,
dn
d
d
POW
x
xx
ddxHdn
Using the Hessian
In particular, if
dn
ddxf ...)( 21
In particular, then
222
21))(( d
ndd
f cxHDET
This gives the algorithm for equivalence to
Equivalence to Problem restatement: Given an n-variate polynomial of degree d, determine if there exist n linearly independent affine functions such that
),...,,()( 21, ndnSYMxf
Fact: is a multilinear polynomial, so
].[ allfor 0,2 niSYM dni
Fact: All the other second-order partial derivatives of are linearly independent.
Outline of Algorithm
Input: A polynomial 1. Compute all the second order partial derivatives of .
2. Compute all the linear dependencies between these second order partials of f.
3. This gives us a linear space of second order differential operators which vanish at f.
4. A second order differential operator naturally corresponds to a matrix. Find a basis of the corresponding linear space of matrices consisting of rank one matrices.
Equivalence to Problem restatement: Given an -variate polynomial of degree n, determine if there exist n linearly independent affine functions such that
),...,,()( 221 nnPERMxf
Fact: The group of symmetries of is continuous.
The following approach was suggested by Mulmuley and Sohoni.
Symmetries of
nnnn
n
nnnn
n
n
xxx
xxxxxx
PERM
xxx
xxxxxx
PERM
21
211
211
211
11211
21
22221
11211
Fact: If we take to be aribtarily close to 1 then we get a matrix A arbitarily close to identity such that
)()( xPERMxAPERM nn
Symmetries of
Let be a formal variable with . Then there exist nontrivial matrices A such that
)())1(( xPERMxAPERM nn
Fact: For any polynomial , the set of matrices A such that
forms a vector space. )())1(( xfxAf
Fact: For a given , a basis for this space can be computed in random polynomial time.
Outline of Algorithm
Input: A polynomial 1. Compute a basis for the space of matrices A satisfying .
2. Let the basis be . These matrices act on an dimensional vector space V.
3. Find all subspaces such that for all .
4. Infer the appropriate equivalence of and from the 1-dimensional invariant subspaces U.
That’s all fine, but what about POLY_PROJECTION?
Theorem (Kaltofen): Projections of can be reconstructed efficiently. (This is just polynomial factoring)
Theorem (follows quickly from the work of Kleppe): Given a univariate polynomial of degree d and an integer s, we can efficiently compute ’s and ’s (if they exist) such that
dss
dd bxabxabxaxf )(...)()()( 2211
Scott’s conjecture
Conjecture (Scott Aaronson): If are random m-variate affine functions then is indistuinguishable from a random m-variate polynomial of degree n.
In other words, solving random instances of projections of is conjecturally hard.
Digression: Background of Scott’s conjecture
Observation: All the known bound proofs (for projections of a family of polynomials ) follow the following strategy:
Step 1. Find an “efficiently computable” property P that is satisfied by projections of small polynomials from .
Step 2. (Relatively easy) Find an explicit polynomial not having the property P.
Example: Mignon and Ressayre
Theorem (Mignon and Ressayre): If is a projection of then m is at least .
Lemma: Let be a projection of . Then for any zero of ,
maH f 2))((Rank
Example: Projections of
Lemma (implicit in Grigoriev-Karpinski): Let be a projection of . Then the set of all possible partial derivatives of contain at most linearly independent polynomials.
Consequences of Scott’s conjecture
• If the conjecture is true then no current proof technique will yield superpolynomial formula size lower bound.
• If the conjecture is false then we would have obtained a good handle on the determinant versus permanent conjecture.
Probing a little deeper
• For families of polynomials such as we have lower bounds.
• Given , where the ’s are m-variate affine functions randomly chosen, can we efficiently recover the ’s?
POLY_PROJECTION on the average
Theorem: Projections of can be reconstructed efficiently on the average.
Theorem: For bounded n, projections of can be reconstructed efficiently on the average.
Summary• POLY_PROJECTION and POLY_EQUIVALENCE are
difficult computational problems in general.
• Empirically, for most families of polynomials that we actually care about and/or encounter in practice, we can solve POLY_EQUIVALENCE efficiently.
• Empirically, efficient average-case algorithms for POLY_PROJECTION of some family seem to be closely related to lower bounds for projections of .
Conclusion: An easier(?) open problem
Conjecture (Amir Shpilka): If is a projection of then .
THE END