5,576 research outputs found
Resolution over Linear Equations and Multilinear Proofs
We develop and study the complexity of propositional proof systems of varying
strength extending resolution by allowing it to operate with disjunctions of
linear equations instead of clauses. We demonstrate polynomial-size refutations
for hard tautologies like the pigeonhole principle, Tseitin graph tautologies
and the clique-coloring tautologies in these proof systems. Using the
(monotone) interpolation by a communication game technique we establish an
exponential-size lower bound on refutations in a certain, considerably strong,
fragment of resolution over linear equations, as well as a general polynomial
upper bound on (non-monotone) interpolants in this fragment.
We then apply these results to extend and improve previous results on
multilinear proofs (over fields of characteristic 0), as studied in
[RazTzameret06]. Specifically, we show the following:
1. Proofs operating with depth-3 multilinear formulas polynomially simulate a
certain, considerably strong, fragment of resolution over linear equations.
2. Proofs operating with depth-3 multilinear formulas admit polynomial-size
refutations of the pigeonhole principle and Tseitin graph tautologies. The
former improve over a previous result that established small multilinear proofs
only for the \emph{functional} pigeonhole principle. The latter are different
than previous proofs, and apply to multilinear proofs of Tseitin mod p graph
tautologies over any field of characteristic 0.
We conclude by connecting resolution over linear equations with extensions of
the cutting planes proof system.Comment: 44 page
Static Data Structure Lower Bounds Imply Rigidity
We show that static data structure lower bounds in the group (linear) model
imply semi-explicit lower bounds on matrix rigidity. In particular, we prove
that an explicit lower bound of on the cell-probe
complexity of linear data structures in the group model, even against
arbitrarily small linear space , would already imply a
semi-explicit () construction of rigid matrices with
significantly better parameters than the current state of art (Alon, Panigrahy
and Yekhanin, 2009). Our results further assert that polynomial () data structure lower bounds against near-optimal space, would
imply super-linear circuit lower bounds for log-depth linear circuits (a
four-decade open question). In the succinct space regime , we show
that any improvement on current cell-probe lower bounds in the linear model
would also imply new rigidity bounds. Our results rely on a new connection
between the "inner" and "outer" dimensions of a matrix (Paturi and Pudlak,
2006), and on a new reduction from worst-case to average-case rigidity, which
is of independent interest
Equivalence of Systematic Linear Data Structures and Matrix Rigidity
Recently, Dvir, Golovnev, and Weinstein have shown that sufficiently strong
lower bounds for linear data structures would imply new bounds for rigid
matrices. However, their result utilizes an algorithm that requires an
oracle, and hence, the rigid matrices are not explicit. In this work, we derive
an equivalence between rigidity and the systematic linear model of data
structures. For the -dimensional inner product problem with queries, we
prove that lower bounds on the query time imply rigidity lower bounds for the
query set itself. In particular, an explicit lower bound of
for redundant storage bits would
yield better rigidity parameters than the best bounds due to Alon, Panigrahy,
and Yekhanin. We also prove a converse result, showing that rigid matrices
directly correspond to hard query sets for the systematic linear model. As an
application, we prove that the set of vectors obtained from rank one binary
matrices is rigid with parameters matching the known results for explicit sets.
This implies that the vector-matrix-vector problem requires query time
for redundancy in the systematic linear
model, improving a result of Chakraborty, Kamma, and Larsen. Finally, we prove
a cell probe lower bound for the vector-matrix-vector problem in the high error
regime, improving a result of Chattopadhyay, Kouck\'{y}, Loff, and
Mukhopadhyay.Comment: 23 pages, 1 tabl
Min-Rank Conjecture for Log-Depth Circuits
A completion of an m-by-n matrix A with entries in {0,1,*} is obtained by
setting all *-entries to constants 0 or 1. A system of semi-linear equations
over GF(2) has the form Mx=f(x), where M is a completion of A and f:{0,1}^n -->
{0,1}^m is an operator, the i-th coordinate of which can only depend on
variables corresponding to *-entries in the i-th row of A. We conjecture that
no such system can have more than 2^{n-c\cdot mr(A)} solutions, where c>0 is an
absolute constant and mr(A) is the smallest rank over GF(2) of a completion of
A. The conjecture is related to an old problem of proving super-linear lower
bounds on the size of log-depth boolean circuits computing linear operators x
--> Mx. The conjecture is also a generalization of a classical question about
how much larger can non-linear codes be than linear ones. We prove some special
cases of the conjecture and establish some structural properties of solution
sets.Comment: 22 pages, to appear in: J. Comput.Syst.Sci
Algebraic and Combinatorial Methods in Computational Complexity
Computational Complexity is concerned with the resources that are required for algorithms to detect properties of combinatorial objects and structures. It has often proven true that the best way to argue about these combinatorial objects is by establishing a connection (perhaps approximate) to a more well-behaved algebraic setting. Indeed, many of the deepest and most powerful results in Computational Complexity rely on algebraic proof techniques. The Razborov-Smolensky polynomial-approximation method for proving constant-depth circuit lower bounds, the PCP characterization of NP, and the Agrawal-Kayal-Saxena polynomial-time primality test are some of the most prominent examples. The algebraic theme continues in some of the most exciting recent progress in computational complexity. There have been significant recent advances in algebraic circuit lower bounds, and the so-called chasm at depth 4 suggests that the restricted models now being considered are not so far from ones that would lead to a general result. There have been similar successes concerning the related problems of polynomial identity testing and circuit reconstruction in the algebraic model (and these are tied to central questions regarding the power of randomness in computation). Another surprising connection is that the algebraic techniques invented to show lower bounds now prove useful to develop efficient algorithms. For example, Williams showed how to use the polynomial method to obtain faster all-pair-shortest-path algorithms. This emphases once again the central role of algebra in computer science. The seminar aims to capitalize on recent progress and bring together researchers who are using a diverse array of algebraic methods in a variety of settings. Researchers in these areas are relying on ever more sophisticated and specialized mathematics and this seminar can play an important role in educating a diverse community about the latest new techniques, spurring further progress
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