614 research outputs found
Bounded-width polynomial-size branching programs recognize exactly those languages in NC1
AbstractWe show that any language recognized by an NC1 circuit (fan-in 2, depth O(log n)) can be recognized by a width-5 polynomial-size branching program. As any bounded-width polynomial-size branching program can be simulated by an NC1 circuit, we have that the class of languages recognized by such programs is exactly nonuniform NC1. Further, following Ruzzo (J. Comput. System Sci. 22 (1981), 365–383) and Cook (Inform. and Control 64 (1985) 2–22), if the branching programs are restricted to be ATIME(logn)-uniform, they recognize the same languages as do ATIME(log n)-uniform NC1 circuits, that is, those languages in ATIME(log n). We also extend the method of proof to investigate the complexity of the word problem for a fixed permutation group and show that polynomial size circuits of width 4 also recognize exactly nonuniform NC1
Balancing Bounded Treewidth Circuits
Algorithmic tools for graphs of small treewidth are used to address questions
in complexity theory. For both arithmetic and Boolean circuits, it is shown
that any circuit of size and treewidth can be
simulated by a circuit of width and size , where , if , and otherwise. For our main construction,
we prove that multiplicatively disjoint arithmetic circuits of size
and treewidth can be simulated by bounded fan-in arithmetic formulas of
depth . From this we derive the analogous statement for
syntactically multilinear arithmetic circuits, which strengthens a theorem of
Mahajan and Rao. As another application, we derive that constant width
arithmetic circuits of size can be balanced to depth ,
provided certain restrictions are made on the use of iterated multiplication.
Also from our main construction, we derive that Boolean bounded fan-in circuits
of size and treewidth can be simulated by bounded fan-in
formulas of depth . This strengthens in the non-uniform setting
the known inclusion that . Finally, we apply our
construction to show that {\sc reachability} for directed graphs of bounded
treewidth is in
Efficient Circuit Simulation in MapReduce
The MapReduce framework has firmly established itself as one of the most widely used parallel computing platforms for processing big data on tera- and peta-byte scale. Approaching it from a theoretical standpoint has proved to be notoriously difficult, however. In continuation of Goodrich et al.\u27s early efforts, explicitly espousing the goal of putting the MapReduce framework on footing equal to that of long-established models such as the PRAM, we investigate the obvious complexity question of how the computational power of MapReduce algorithms compares to that of combinational Boolean circuits commonly used for parallel computations. Relying on the standard MapReduce model introduced by Karloff et al. a decade ago, we develop an intricate simulation technique to show that any problem in NC (i.e., a problem solved by a logspace-uniform family of Boolean circuits of polynomial size and a depth polylogarithmic in the input size) can be solved by a MapReduce computation in O(T(n)/log n) rounds, where n is the input size and T(n) is the depth of the witnessing circuit family. Thus, we are able to closely relate the standard, uniform NC hierarchy modeling parallel computations to the deterministic MapReduce hierarchy DMRC by proving that NC^{i+1} subseteq DMRC^i for all i in N. Besides the theoretical significance, this result has important applied aspects as well. In particular, we show for all problems in NC^1 - many practically relevant ones, such as integer multiplication and division and the parity function, being among these - how to solve them in a constant number of deterministic MapReduce rounds
Trading Determinism for Time in Space Bounded Computations
Savitch showed in that nondeterministic logspace (NL) is contained in
deterministic space but his algorithm requires
quasipolynomial time. The question whether we can have a deterministic
algorithm for every problem in NL that requires polylogarithmic space and
simultaneously runs in polynomial time was left open.
In this paper we give a partial solution to this problem and show that for
every language in NL there exists an unambiguous nondeterministic algorithm
that requires space and simultaneously runs in
polynomial time.Comment: Accepted in MFCS 201
Evaluating Matrix Circuits
The circuit evaluation problem (also known as the compressed word problem)
for finitely generated linear groups is studied. The best upper bound for this
problem is , which is shown by a reduction to polynomial
identity testing. Conversely, the compressed word problem for the linear group
is equivalent to polynomial identity testing. In
the paper, it is shown that the compressed word problem for every finitely
generated nilpotent group is in . Within
the larger class of polycyclic groups we find examples where the compressed
word problem is at least as hard as polynomial identity testing for skew
arithmetic circuits
Unitary Branching Programs: Learnability and Lower Bounds
Bounded width branching programs are a formalism that can be used to capture the notion of non-uniform constant-space computation. In this work, we study a generalized version of bounded width branching programs where instructions are defined by unitary matrices of bounded dimension. We introduce a new learning framework for these branching programs that leverages on a combination of local search techniques with gradient descent over Riemannian manifolds. We also show that gapped, read-once branching programs of bounded dimension can be learned with a polynomial number of queries in the presence of a teacher. Finally, we provide explicit near-quadratic size lower-bounds for bounded-dimension unitary branching programs, and exponential size lower-bounds for bounded-dimension read-once gapped unitary branching programs. The first lower bound is proven using a combination of Neciporuk’s lower bound technique with classic results from algebraic geometry. The second lower bound is proven within the framework of communication complexity theory.publishedVersio
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