203 research outputs found
Arithmetic Circuit Lower Bounds via MaxRank
We introduce the polynomial coefficient matrix and identify maximum rank of
this matrix under variable substitution as a complexity measure for
multivariate polynomials. We use our techniques to prove super-polynomial lower
bounds against several classes of non-multilinear arithmetic circuits. In
particular, we obtain the following results :
As our main result, we prove that any homogeneous depth-3 circuit for
computing the product of matrices of dimension requires
size. This improves the lower bounds by Nisan and
Wigderson(1995) when .
There is an explicit polynomial on variables and degree at most
for which any depth-3 circuit of product dimension at most
(dimension of the space of affine forms feeding into each
product gate) requires size . This generalizes the lower bounds
against diagonal circuits proved by Saxena(2007). Diagonal circuits are of
product dimension 1.
We prove a lower bound on the size of product-sparse
formulas. By definition, any multilinear formula is a product-sparse formula.
Thus, our result extends the known super-polynomial lower bounds on the size of
multilinear formulas by Raz(2006).
We prove a lower bound on the size of partitioned arithmetic
branching programs. This result extends the known exponential lower bound on
the size of ordered arithmetic branching programs given by Jansen(2008).Comment: 22 page
A Near-Optimal Depth-Hierarchy Theorem for Small-Depth Multilinear Circuits
We study the size blow-up that is necessary to convert an algebraic circuit
of product-depth to one of product-depth in the multilinear
setting.
We show that for every positive
there is an explicit multilinear polynomial on variables
that can be computed by a multilinear formula of product-depth and
size , but not by any multilinear circuit of product-depth and
size less than . This result is tight up to the
constant implicit in the double exponent for all
This strengthens a result of Raz and Yehudayoff (Computational Complexity
2009) who prove a quasipolynomial separation for constant-depth multilinear
circuits, and a result of Kayal, Nair and Saha (STACS 2016) who give an
exponential separation in the case
Our separating examples may be viewed as algebraic analogues of variants of
the Graph Reachability problem studied by Chen, Oliveira, Servedio and Tan
(STOC 2016), who used them to prove lower bounds for constant-depth Boolean
circuits
Separation Between Read-once Oblivious Algebraic Branching Programs (ROABPs) and Multilinear Depth Three Circuits
We show an exponential separation between two well-studied models of algebraic computation, namely read-once oblivious algebraic branching programs (ROABPs) and multilinear depth three circuits. In particular we show the following:
1. There exists an explicit n-variate polynomial computable by linear sized multilinear depth three circuits (with only two product gates) such that every ROABP computing it requires 2^{Omega(n)} size.
2. Any multilinear depth three circuit computing IMM_{n,d} (the iterated matrix multiplication polynomial formed by multiplying d, n * n symbolic matrices) has n^{Omega(d)} size. IMM_{n,d} can be easily computed by a poly(n,d) sized ROABP.
3. Further, the proof of 2 yields an exponential separation between multilinear depth four and multilinear depth three circuits: There is an explicit n-variate, degree d polynomial computable by a poly(n,d) sized multilinear depth four circuit such that any multilinear depth three circuit computing it has size n^{Omega(d)}. This improves upon the quasi-polynomial separation result by Raz and Yehudayoff [2009] between these two models.
The hard polynomial in 1 is constructed using a novel application of expander graphs in conjunction with the evaluation dimension measure used previously in Nisan [1991], Raz [2006,2009], Raz and Yehudayoff [2009], and Forbes and Shpilka [2013], while 2 is proved via a new adaptation of the dimension of the partial derivatives measure used by Nisan and Wigderson [1997]. Our lower bounds hold over any field
Identity Testing and Lower Bounds for Read-k Oblivious Algebraic Branching Programs
Read-k oblivious algebraic branching programs are a natural generalization of the well-studied model of read-once oblivious algebraic branching program (ROABPs). In this work, we give an exponential lower bound of exp(n/k^{O(k)}) on the width of any read-k oblivious ABP computing some explicit multilinear polynomial f that is computed by a polynomial size depth-3 circuit. We also study the polynomial identity testing (PIT) problem for this model and obtain a white-box subexponential-time PIT algorithm. The algorithm runs in time 2^{~O(n^{1-1/2^{k-1}})} and needs white box access only to know the order in which the variables appear in the ABP
Separating ABPs and Some Structured Formulas in the Non-Commutative Setting
The motivating question for this work is a long standing open problem, posed
by Nisan (1991), regarding the relative powers of algebraic branching programs
(ABPs) and formulas in the non-commutative setting. Even though the general
question continues to remain open, we make some progress towards its
resolution. To that effect, we generalise the notion of ordered polynomials in
the non-commutative setting (defined by \Hrubes, Wigderson and Yehudayoff
(2011)) to define abecedarian polynomials and models that naturally compute
them.
Our main contribution is a possible new approach towards separating formulas
and ABPs in the non-commutative setting, via lower bounds against abecedarian
formulas. In particular, we show the following.
There is an explicit n-variate degree d abecedarian polynomial
such that 1. can be computed by an abecedarian ABP of size O(nd);
2. any abecedarian formula computing must have size that is
super-polynomial in n.
We also show that a super-polynomial lower bound against abecedarian formulas
for would separate the powers of formulas and ABPs in the
non-commutative setting
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