47,618 research outputs found
Separating decision tree complexity from subcube partition complexity
The subcube partition model of computation is at least as powerful as
decision trees but no separation between these models was known. We show that
there exists a function whose deterministic subcube partition complexity is
asymptotically smaller than its randomized decision tree complexity, resolving
an open problem of Friedgut, Kahn, and Wigderson (2002). Our lower bound is
based on the information-theoretic techniques first introduced to lower bound
the randomized decision tree complexity of the recursive majority function.
We also show that the public-coin partition bound, the best known lower bound
method for randomized decision tree complexity subsuming other general
techniques such as block sensitivity, approximate degree, randomized
certificate complexity, and the classical adversary bound, also lower bounds
randomized subcube partition complexity. This shows that all these lower bound
techniques cannot prove optimal lower bounds for randomized decision tree
complexity, which answers an open question of Jain and Klauck (2010) and Jain,
Lee, and Vishnoi (2014).Comment: 16 pages, 1 figur
Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory
We present an information-theoretic approach to lower bound the oracle
complexity of nonsmooth black box convex optimization, unifying previous lower
bounding techniques by identifying a combinatorial problem, namely string
guessing, as a single source of hardness. As a measure of complexity we use
distributional oracle complexity, which subsumes randomized oracle complexity
as well as worst-case oracle complexity. We obtain strong lower bounds on
distributional oracle complexity for the box , as well as for the
-ball for (for both low-scale and large-scale regimes),
matching worst-case upper bounds, and hence we close the gap between
distributional complexity, and in particular, randomized complexity, and
worst-case complexity. Furthermore, the bounds remain essentially the same for
high-probability and bounded-error oracle complexity, and even for combination
of the two, i.e., bounded-error high-probability oracle complexity. This
considerably extends the applicability of known bounds
Randomized and Quantum Algorithms Yield a Speed-Up for Initial-Value Problems
Quantum algorithms and complexity have recently been studied not only for
discrete, but also for some numerical problems. Most attention has been paid so
far to the integration problem, for which a speed-up is shown by quantum
computers with respect to deterministic and randomized algorithms on a
classical computer. In this paper we deal with the randomized and quantum
complexity of initial-value problems. For this nonlinear problem, we show that
both randomized and quantum algorithms yield a speed-up over deterministic
algorithms. Upper bounds on the complexity in the randomized and quantum
settings are shown by constructing algorithms with a suitable cost, where the
construction is based on integral information. Lower bounds result from the
respective bounds for the integration problem.Comment: LaTeX v. 2.09, 13 page
Almost Optimal Solution of Initial-Value Problems by Randomized and Quantum Algorithms
We establish essentially optimal bounds on the complexity of initial-value
problems in the randomized and quantum settings. For this purpose we define a
sequence of new algorithms whose error/cost properties improve from step to
step. These algorithms yield new upper complexity bounds, which differ from
known lower bounds by only an arbitrarily small positive parameter in the
exponent, and a logarithmic factor. In both the randomized and quantum
settings, initial-value problems turn out to be essentially as difficult as
scalar integration.Comment: 16 pages, minor presentation change
The Partition Bound for Classical Communication Complexity and Query Complexity
We describe new lower bounds for randomized communication complexity and
query complexity which we call the partition bounds. They are expressed as the
optimum value of linear programs. For communication complexity we show that the
partition bound is stronger than both the rectangle/corruption bound and the
\gamma_2/generalized discrepancy bounds. In the model of query complexity we
show that the partition bound is stronger than the approximate polynomial
degree and classical adversary bounds. We also exhibit an example where the
partition bound is quadratically larger than polynomial degree and classical
adversary bounds.Comment: 28 pages, ver. 2, added conten
Optimal randomized multilevel algorithms for infinite-dimensional integration on function spaces with ANOVA-type decomposition
In this paper, we consider the infinite-dimensional integration problem on
weighted reproducing kernel Hilbert spaces with norms induced by an underlying
function space decomposition of ANOVA-type. The weights model the relative
importance of different groups of variables. We present new randomized
multilevel algorithms to tackle this integration problem and prove upper bounds
for their randomized error. Furthermore, we provide in this setting the first
non-trivial lower error bounds for general randomized algorithms, which, in
particular, may be adaptive or non-linear. These lower bounds show that our
multilevel algorithms are optimal. Our analysis refines and extends the
analysis provided in [F. J. Hickernell, T. M\"uller-Gronbach, B. Niu, K.
Ritter, J. Complexity 26 (2010), 229-254], and our error bounds improve
substantially on the error bounds presented there. As an illustrative example,
we discuss the unanchored Sobolev space and employ randomized quasi-Monte Carlo
multilevel algorithms based on scrambled polynomial lattice rules.Comment: 31 pages, 0 figure
Improved Bounds on the Randomized and Quantum Complexity of Initial-Value Problems
We deal with the problem, initiated in [8], of finding randomized and quantum
complexity of initial-value problems. We showed in [8] that a speed-up in both
settings over the worst-case deterministic complexity is possible. In the
present paper we prove, by defining new algorithms, that further improvement in
upper bounds on the randomized and quantum complexity can be achieved. In the
H\"older class of right-hand side functions with r continuous bounded partial
derivatives, with r-th derivative being a H\"older function with exponent \rho,
the \epsilon-complexity is shown to be O((1/\epsilon)^{1/(r+\rho+1/3)}) in the
randomized setting, and O((1/\epsilon)^{1/(r+\rho+1/2)}) on a quantum computer
(up to logarithmic factors). This is an improvement for the general problem
over the results from [8]. The gap still remaining between upper and lower
bounds on the complexity is further discussed for a special problem. We
consider scalar autonomous problems, with the aim of computing the solution at
the end point of the interval of integration. For this problem, we fill up the
gap by establishing (essentially) matching upper and lower complexity bounds.
We show that the complexity in this case is of order
(1/\epsilon)^{1/(r+\rho+1/2)} in the randomized setting, and
(1/\epsilon)^{1/(r+\rho+1)} in the quantum setting (again up to logarithmic
factors).Comment: 17 pages, extended version (new section added), to appear in the
Journal of Complexit
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