3,776 research outputs found
Lower Bounds on the Bounded Coefficient Complexity of Bilinear Maps
We prove lower bounds of order for both the problem to multiply
polynomials of degree , and to divide polynomials with remainder, in the
model of bounded coefficient arithmetic circuits over the complex numbers.
These lower bounds are optimal up to order of magnitude. The proof uses a
recent idea of R. Raz [Proc. 34th STOC 2002] proposed for matrix
multiplication. It reduces the linear problem to multiply a random circulant
matrix with a vector to the bilinear problem of cyclic convolution. We treat
the arising linear problem by extending J. Morgenstern's bound [J. ACM 20, pp.
305-306, 1973] in a unitarily invariant way. This establishes a new lower bound
on the bounded coefficient complexity of linear forms in terms of the singular
values of the corresponding matrix. In addition, we extend these lower bounds
for linear and bilinear maps to a model of circuits that allows a restricted
number of unbounded scalar multiplications.Comment: 19 page
Asymptotic tensor rank of graph tensors: beyond matrix multiplication
We present an upper bound on the exponent of the asymptotic behaviour of the
tensor rank of a family of tensors defined by the complete graph on
vertices. For , we show that the exponent per edge is at most 0.77,
outperforming the best known upper bound on the exponent per edge for matrix
multiplication (), which is approximately 0.79. We raise the question
whether for some the exponent per edge can be below , i.e. can
outperform matrix multiplication even if the matrix multiplication exponent
equals 2. In order to obtain our results, we generalise to higher order tensors
a result by Strassen on the asymptotic subrank of tight tensors and a result by
Coppersmith and Winograd on the asymptotic rank of matrix multiplication. Our
results have applications in entanglement theory and communication complexity
Global Optimization for Value Function Approximation
Existing value function approximation methods have been successfully used in
many applications, but they often lack useful a priori error bounds. We propose
a new approximate bilinear programming formulation of value function
approximation, which employs global optimization. The formulation provides
strong a priori guarantees on both robust and expected policy loss by
minimizing specific norms of the Bellman residual. Solving a bilinear program
optimally is NP-hard, but this is unavoidable because the Bellman-residual
minimization itself is NP-hard. We describe and analyze both optimal and
approximate algorithms for solving bilinear programs. The analysis shows that
this algorithm offers a convergent generalization of approximate policy
iteration. We also briefly analyze the behavior of bilinear programming
algorithms under incomplete samples. Finally, we demonstrate that the proposed
approach can consistently minimize the Bellman residual on simple benchmark
problems
Sparse Gr\"obner Bases: the Unmixed Case
Toric (or sparse) elimination theory is a framework developped during the
last decades to exploit monomial structures in systems of Laurent polynomials.
Roughly speaking, this amounts to computing in a \emph{semigroup algebra},
\emph{i.e.} an algebra generated by a subset of Laurent monomials. In order to
solve symbolically sparse systems, we introduce \emph{sparse Gr\"obner bases},
an analog of classical Gr\"obner bases for semigroup algebras, and we propose
sparse variants of the and FGLM algorithms to compute them. Our prototype
"proof-of-concept" implementation shows large speed-ups (more than 100 for some
examples) compared to optimized (classical) Gr\"obner bases software. Moreover,
in the case where the generating subset of monomials corresponds to the points
with integer coordinates in a normal lattice polytope and under regularity assumptions, we prove complexity bounds which depend
on the combinatorial properties of . These bounds yield new
estimates on the complexity of solving -dim systems where all polynomials
share the same Newton polytope (\emph{unmixed case}). For instance, we
generalize the bound on the maximal degree in a Gr\"obner
basis of a -dim. bilinear system with blocks of variables of sizes
to the multilinear case: . We also propose
a variant of Fr\"oberg's conjecture which allows us to estimate the complexity
of solving overdetermined sparse systems.Comment: 20 pages, Corollary 6.1 has been corrected, ISSAC 2014, Kobe : Japan
(2014
Improved method for finding optimal formulae for bilinear maps in a finite field
In 2012, Barbulescu, Detrey, Estibals and Zimmermann proposed a new framework
to exhaustively search for optimal formulae for evaluating bilinear maps, such
as Strassen or Karatsuba formulae. The main contribution of this work is a new
criterion to aggressively prune useless branches in the exhaustive search, thus
leading to the computation of new optimal formulae, in particular for the short
product modulo X 5 and the circulant product modulo (X 5 -- 1). Moreover , we
are able to prove that there is essentially only one optimal decomposition of
the product of 3 x 2 by 2 x 3 matrices up to the action of some group of
automorphisms
Time-parallel iterative solvers for parabolic evolution equations
We present original time-parallel algorithms for the solution of the implicit
Euler discretization of general linear parabolic evolution equations with
time-dependent self-adjoint spatial operators. Motivated by the inf-sup theory
of parabolic problems, we show that the standard nonsymmetric time-global
system can be equivalently reformulated as an original symmetric saddle-point
system that remains inf-sup stable with respect to the same natural parabolic
norms. We then propose and analyse an efficient and readily implementable
parallel-in-time preconditioner to be used with an inexact Uzawa method. The
proposed preconditioner is non-intrusive and easy to implement in practice, and
also features the key theoretical advantages of robust spectral bounds, leading
to convergence rates that are independent of the number of time-steps, final
time, or spatial mesh sizes, and also a theoretical parallel complexity that
grows only logarithmically with respect to the number of time-steps. Numerical
experiments with large-scale parallel computations show the effectiveness of
the method, along with its good weak and strong scaling properties
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