共查询到20条相似文献,搜索用时 46 毫秒
1.
Daniel M. Kane 《Computational Complexity》2011,20(2):389-412
We prove asymptotically optimal bounds on the Gaussian noise sensitivity and Gaussian surface area of degree-d polynomial threshold functions. In particular, we show that for f a degree-d polynomial threshold function that the Gaussian noise sensitivity of f with parameter e{\epsilon} is at most
\fracdarcsin(?{2e-e2})p{\frac{d\arcsin\left(\sqrt{2\epsilon-\epsilon^2}\right)}{\pi}} . This bound translates into an optimal bound on the Gaussian surface area of such functions, namely that the Gaussian surface
area is at most
\fracd?{2p}{\frac{d}{\sqrt{2\pi}}} . Finally, we note that the later result implies bounds on the runtime of agnostic learning algorithms for polynomial threshold
functions. 相似文献
2.
We design an adiabatic quantum algorithm for the counting problem, i.e., approximating the proportion, α, of the marked items
in a given database. As the quantum system undergoes a designed cyclic adiabatic evolution, it acquires a Berry phase 2πα. By estimating the Berry phase, we can approximate α, and solve the problem. For an error bound e{\epsilon}, the algorithm can solve the problem with cost of order
(\frac1e)3/2{(\frac{1}{\epsilon})^{3/2}}, which is not as good as the optimal algorithm in the quantum circuit model, but better than the classical random algorithm.
Moreover, since the Berry phase is a purely geometric feature, the result may be robust to decoherence and resilient to certain
noise. 相似文献
3.
Given an undirected graph and 0 £ e £ 1{0\le\epsilon\le1}, a set of nodes is called an e{\epsilon}-near clique if all but an e{\epsilon} fraction of the pairs of nodes in the set have a link between them. In this paper we present a fast synchronous network algorithm
that uses small messages and finds a near-clique. Specifically, we present a constant-time algorithm that finds, with constant
probability of success, a linear size e{\epsilon}-near clique if there exists an e3{\epsilon^3}-near clique of linear size in the graph. The algorithm uses messages of O(log n) bits. The failure probability can be reduced to n
−Ω(1) by increasing the time complexity by a logarithmic factor, and the algorithm also works if the graph contains a clique of
size Ω(n/(log log n)
α
) for some a ? (0,1){\alpha \in (0,1)}. Our approach is based on a new idea of adapting property testing algorithms to the distributed setting. 相似文献
4.
Given a “black box” function to evaluate an unknown rational polynomial
f ? \mathbbQ[x]f \in {\mathbb{Q}}[x] at points modulo a prime p, we exhibit algorithms to compute the representation of the polynomial in the sparsest shifted power basis. That is, we determine
the sparsity $t \in {\mathbb{Z}}_{>0}$t \in {\mathbb{Z}}_{>0}, the shift
a ? \mathbbQ\alpha \in {\mathbb{Q}}, the exponents 0 £ e1 < e2 < ? < et{0 \leq e_{1} < e_{2} < \cdots < e_{t}}, and the coefficients
c1, ?, ct ? \mathbbQ \{0}c_{1}, \ldots , c_{t} \in {\mathbb{Q}} \setminus \{0\} such that
f(x) = c1(x-a)e1+c2(x-a)e2+ ?+ct(x-a)etf(x) = c_{1}(x-\alpha)^{e_{1}}+c_{2}(x-\alpha)^{e_{2}}+ \cdots +c_{t}(x-\alpha)^{e_{t}} 相似文献
5.
Alastair A. Abbott 《Natural computing》2012,11(1):3-11
The Deutsch–Jozsa problem is one of the most basic ways to demonstrate the power of quantum computation. Consider a Boolean
function f : {0, 1}
n
→ {0, 1} and suppose we have a black-box to compute f. The Deutsch–Jozsa problem is to determine if f is constant (i.e. f(x) = const, "x ? {0,1}nf(x) = \hbox {const, } \forall x \in \{0,1\}^n) or if f is balanced (i.e. f(x) = 0 for exactly half the possible input strings x ? {0,1}nx \in \{0,1\}^n) using as few calls to the black-box computing f as is possible, assuming f is guaranteed to be constant or balanced. Classically it appears that this requires at least 2
n−1 + 1 black-box calls in the worst case, but the well known quantum solution solves the problem with probability one in exactly
one black-box call. It has been found that in some cases the algorithm can be de-quantised into an equivalent classical, deterministic
solution. We explore the ability to extend this de-quantisation to further cases, and examine with more detail when de-quantisation
is possible, both with respect to the Deutsch–Jozsa problem, as well as in more general cases. 相似文献
6.
Thomas Watson 《Computational Complexity》2013,22(4):727-769
We define a combinatorial checkerboard to be a function f : {1, . . . , m} d → {1,?1} of the form ${f(u_1,\ldots,u_d)=\prod_{i=1}^df_i(u_i)}$ for some functions f i : {1, . . . , m} → {1,?1}. This is a variant of combinatorial rectangles, which can be defined in the same way but using {0, 1} instead of {1,?1}. We consider the problem of constructing explicit pseudorandom generators for combinatorial checkerboards. This is a generalization of small-bias generators, which correspond to the case m = 2. We construct a pseudorandom generator that ${\epsilon}$ -fools all combinatorial checkerboards with seed length ${O\bigl(\log m+\log d\cdot\log\log d+\log^{3/2} \frac{1}{\epsilon}\bigr)}$ . Previous work by Impagliazzo, Nisan, and Wigderson implies a pseudorandom generator with seed length ${O\bigl(\log m+\log^2d+\log d\cdot\log\frac{1}{\epsilon}\bigr)}$ . Our seed length is better except when ${\frac{1}{\epsilon}\geq d^{\omega(\log d)}}$ . 相似文献
7.
Consider the following model on the spreading of messages. A message initially convinces a set of vertices, called the seeds,
of the Erdős-Rényi random graph G(n,p). Whenever more than a ρ∈(0,1) fraction of a vertex v’s neighbors are convinced of the message, v will be convinced. The spreading proceeds asynchronously until no more vertices can be convinced. This paper derives lower
bounds on the minimum number of initial seeds, min-seed(n,p,d,r)\mathrm{min\hbox{-}seed}(n,p,\delta,\rho), needed to convince a δ∈{1/n,…,n/n} fraction of vertices at the end. In particular, we show that (1) there is a constant β>0 such that min-seed(n,p,d,r)=W(min{d,r}n)\mathrm{min\hbox{-}seed}(n,p,\delta,\rho)=\Omega(\min\{\delta,\rho\}n) with probability 1−n
−Ω(1) for p≥β (ln (e/min {δ,ρ}))/(ρ
n) and (2) min-seed(n,p,d,1/2)=W(dn/ln(e/d))\mathrm{min\hbox{-}seed}(n,p,\delta,1/2)=\Omega(\delta n/\ln(e/\delta)) with probability 1−exp (−Ω(δ
n))−n
−Ω(1) for all p∈[ 0,1 ]. The hidden constants in the Ω notations are independent of p. 相似文献
8.
Alexander A. Sherstov 《Computational Complexity》2010,19(1):135-150
We solve an open problem in communication complexity posed by Kushilevitz and Nisan (1997). Let R∈(f) and $D^\mu_\in
(f)$D^\mu_\in
(f) denote the randomized and μ-distributional communication complexities of f, respectively (∈ a small constant). Yao’s well-known minimax principle states that $R_{\in}(f) = max_\mu
\{D^\mu_\in(f)\}$R_{\in}(f) = max_\mu
\{D^\mu_\in(f)\}. Kushilevitz and Nisan (1997) ask whether this equality is approximately preserved if the maximum is taken over product distributions
only, rather than all distributions μ. We give a strong negative answer to this question. Specifically, we prove the existence
of a function f : {0, 1}n ×{0, 1}n ? {0, 1}f : \{0, 1\}^n \times \{0, 1\}^n \rightarrow \{0, 1\} for which maxμ product {Dm ? (f)} = Q(1) but R ? (f) = Q(n)\{D^\mu_\in (f)\} = \Theta(1) \,{\textrm but}\, R_{\in} (f) = \Theta(n). We also obtain an exponential separation between the statistical query dimension and signrank, solving a problem previously
posed by the author (2007). 相似文献
9.
Given an alphabet Σ={1,2,…,|Σ|} text string T∈Σ
n
and a pattern string P∈Σ
m
, for each i=1,2,…,n−m+1 define L
p
(i) as the p-norm distance when the pattern is aligned below the text and starts at position i of the text. The problem of pattern matching with L
p
distance is to compute L
p
(i) for every i=1,2,…,n−m+1. We discuss the problem for d=1,2,∞. First, in the case of L
1 matching (pattern matching with an L
1 distance) we show a reduction of the string matching with mismatches problem to the L
1 matching problem and we present an algorithm that approximates the L
1 matching up to a factor of 1+ε, which has an
O(\frac1e2nlogmlog|S|)O(\frac{1}{\varepsilon^{2}}n\log m\log|\Sigma|)
run time. Then, the L
2 matching problem (pattern matching with an L
2 distance) is solved with a simple O(nlog m) time algorithm. Finally, we provide an algorithm that approximates the L
∞ matching up to a factor of 1+ε with a run time of
O(\frac1enlogmlog|S|)O(\frac{1}{\varepsilon}n\log m\log|\Sigma|)
. We also generalize the problem of String Matching with mismatches to have weighted mismatches and present an O(nlog 4
m) algorithm that approximates the results of this problem up to a factor of O(log m) in the case that the weight function is a metric. 相似文献
10.
Philippe Raïpin Parvédy Michel Raynal Corentin Travers 《Theory of Computing Systems》2010,47(1):259-287
The k-set agreement problem is a generalization of the consensus problem: considering a system made up of n processes where each process proposes a value, each non-faulty process has to decide a value such that a decided value is a proposed value, and no more than k different values are decided. It has recently be shown that, in the crash failure model, $\min(\lfloor \frac{f}{k}\rfloor+2,\lfloor \frac{t}{k}\rfloor +1)
11.
Roel Verstappen 《Journal of scientific computing》2011,49(1):94-110
Large eddy simulation (LES) seeks to predict the dynamics of spatially filtered turbulent flows. The very essence is that
the LES-solution contains only scales of size ≥Δ, where Δ denotes some user-chosen length scale. This property enables us
to perform a LES when it is not feasible to compute the full, turbulent solution of the Navier-Stokes equations. Therefore,
in case the large eddy simulation is based on an eddy viscosity model we determine the eddy viscosity such that any scales
of size <Δ are dynamically insignificant. In this paper, we address the following two questions: how much eddy diffusion is
needed to (a) balance the production of scales of size smaller than Δ; and (b) damp any disturbances having a scale of size
smaller than Δ initially. From this we deduce that the eddy viscosity ν
e
has to depend on the invariants
q = \frac12tr(S2)q = \frac{1}{2}\mathrm{tr}(S^{2}) and
r = -\frac13tr(S3)r= -\frac{1}{3}\mathrm{tr}(S^{3}) of the (filtered) strain rate tensor S. The simplest model is then given by
ne = \frac32(D/p)2 |r|/q\nu_{e} = \frac{3}{2}(\Delta/\pi)^{2} |r|/q. This model is successfully tested for a turbulent channel flow (Re
τ
=590). 相似文献
12.
For hyper-rectangles in $\mathbb{R}^{d}$ Auer (1997) proved a PAC bound of $O(\frac{1}{\varepsilon}(d+\log \frac{1}{\delta}))$ , where $\varepsilon$ and $\delta$ are the accuracy and confidence parameters. It is still an open question whether one can obtain the same bound for intersection-closed concept classes of VC-dimension $d$ in general. We present a step towards a solution of this problem showing on one hand a new PAC bound of $O(\frac{1}{\varepsilon}(d\log d + \log \frac{1}{\delta}))$ for arbitrary intersection-closed concept classes, complementing the well-known bounds $O(\frac{1}{\varepsilon}(\log \frac{1}{\delta}+d\log \frac{1}{\varepsilon}))$ and $O(\frac{d}{\varepsilon}\log \frac{1}{\delta})$ of Blumer et al. and (1989) and Haussler, Littlestone and Warmuth (1994). Our bound is established using the closure algorithm, that generates as its hypothesis the intersection of all concepts that are consistent with the positive training examples. On the other hand, we show that many intersection-closed concept classes including e.g. maximum intersection-closed classes satisfy an additional combinatorial property that allows a proof of the optimal bound of $O(\frac{1}{\varepsilon}(d+\log \frac{1}{\delta}))$ . For such improved bounds the choice of the learning algorithm is crucial, as there are consistent learning algorithms that need $\Omega(\frac{1}{\varepsilon}(d\log\frac{1}{\varepsilon} +\log\frac{1}{\delta}))$ examples to learn some particular maximum intersection-closed concept classes. 相似文献
13.
We investigate the diameter
problem in the streaming and sliding-window
models. We show that, for
a stream of nn points or a sliding window of size nn, any exact
algorithm for diameter requires W(n)\Omega(n) bits of
space. We present a simple e\epsilon-approximation algorithm
for computing the diameter in the streaming model. Our main result
is an e\epsilon-approximation algorithm
that maintains the diameter in two dimensions in the sliding-window
model using O((1/e3/2) log3n(logR+loglogn + log(1/e)))O(({1}/{\epsilon^{3/2}}) \log^{3}n(\log R+\log\log n +
\log ({1}/{\epsilon}))) bits of space, where RR is the maximum, over all
windows, of the ratio of the diameter to the minimum non-zero distance
between any two points in the window. 相似文献
14.
We prove that the concept class of disjunctions cannot be pointwise approximated by linear combinations of any small set of
arbitrary real-valued functions. That is, suppose that there exist functions f1, ?, fr\phi_{1}, \ldots , \phi_{r} : {− 1, 1}n →
\mathbbR{\mathbb{R}} with the property that every disjunction f on n variables has $\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi
_{i}\|_{\infty}\leq 1/3$\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi
_{i}\|_{\infty}\leq 1/3 for some reals a1, ?, ar\alpha_{1}, \ldots , \alpha_{r}. We prove that then $r \geq
exp \{\Omega(\sqrt{n})\}$r \geq
exp \{\Omega(\sqrt{n})\}, which is tight. We prove an incomparable lower bound for the concept class of decision lists. For the concept class of majority
functions, we obtain a lower bound of W(2n/n)\Omega(2^{n}/n) , which almost meets the trivial upper bound of 2n for any concept class. These lower bounds substantially strengthen and generalize the polynomial approximation lower bounds of Paturi
(1992) and show that the regression-based agnostic learning algorithm of Kalai et al. (2005) is optimal. 相似文献
15.
In this article we give several new results on the complexity of algorithms that learn Boolean functions from quantum queries
and quantum examples.
16.
We present in this paper an analysis of a semi-Lagrangian second order Backward Difference Formula combined with hp-finite
element method to calculate the numerical solution of convection diffusion equations in ℝ2. Using mesh dependent norms, we prove that the a priori error estimate has two components: one corresponds to the approximation
of the exact solution along the characteristic curves, which is
O(Dt2+hm+1(1+\frac\mathopen|logh|Dt))O(\Delta t^{2}+h^{m+1}(1+\frac{\mathopen{|}\log h|}{\Delta t})); and the second, which is O(Dtp+|| [(u)\vec]-[(u)\vec]h||L¥)O(\Delta t^{p}+\| \vec{u}-\vec{u}_{h}\|_{L^{\infty}}), represents the error committed in the calculation of the characteristic curves. Here, m is the degree of the polynomials in the finite element space, [(u)\vec]\vec{u} is the velocity vector, [(u)\vec]h\vec{u}_{h} is the finite element approximation of [(u)\vec]\vec{u} and p denotes the order of the method employed to calculate the characteristics curves. Numerical examples support the validity
of our estimates. 相似文献
17.
We present an algorithm for testing the k-vertex-connectivity of graphs with the given maximum degree. The time complexity of the algorithm is independent of the number of vertices and edges of graphs. Fixed degree bound d, a graph G with n vertices and a maximum degree at most d is called ε-far from k-vertex-connectivity when at least $\frac{\epsilon dn}{2}$ edges must be added to or removed from G to obtain a k-vertex-connected graph with a maximum degree at most d. The algorithm always accepts every graph that is k-vertex-connected and rejects every graph that is ε-far from k-vertex-connectivity with a probability of at least 2/3. The algorithm runs in $O(d(\frac{c}{\epsilon d})^{k}\log\frac {1}{\epsilon d})$ time (c>1 is a constant) for (k?1)-vertex-connected graphs, and in $O(d(\frac{ck}{\epsilon d})^{k}\log\frac{k}{\epsilon d})$ time (c>1 is a constant) for general graphs. It is the first constant-time k-vertex-connectivity testing algorithm for general k≥4. 相似文献
18.
In this paper, we present an algorithm that utilizes a quadtree data structure to construct a quadrilateral mesh for a simple
polygonal region in which no newly created angle is smaller than
18.43° (=arctan(\frac13)){{18.43}}^{\circ} ({=}\hbox{arctan}(\frac{1}{3})) or greater than
171.86° (=135° + 2arctan(\frac13)){{171.86}}^{\circ} ({=}{{135}}^{\circ} + 2\hbox{arctan}(\frac{1}{3})). This is the first known result, to the best of our knowledge, on a direct quadrilateral mesh generation algorithm with a
provable guarantee on the angles. 相似文献
19.
Emanuele Viola 《Computational Complexity》2005,13(3-4):147-188
We study the complexity of constructing pseudorandom generators (PRGs) from hard functions, focussing on constant-depth circuits. We show that, starting from a function
computable in alternating time O(l) with O(1) alternations that is hard on average (i.e. there is a constant
such that every circuit of size
fails to compute f on at least a 1/poly(l) fraction of inputs) we can construct a
computable by DLOGTIME-uniform constant-depth circuits of size polynomial in n. Such a PRG implies
under DLOGTIME-uniformity. On the negative side, we prove that starting from a worst-case hard function
(i.e. there is a constant
such that every circuit of size
fails to compute f on some input) for every positive constant
there is no black-box construction of a
computable by constant-depth circuits of size polynomial in n. We also study worst-case hardness amplification, which is the related problem of producing an average-case hard function starting from a worst-case hard one. In particular, we deduce that there is no blackbox worst-case hardness amplification within the polynomial time hierarchy. These negative results are obtained by showing that polynomialsize constant-depth circuits cannot compute good extractors and listdecodable codes. 相似文献
20.
Hazem M. Bahig 《The Journal of supercomputing》2008,43(1):99-104
In this paper, we study the merging of two sorted arrays
and
on EREW PRAM with two restrictions: (1) The elements of two arrays are taken from the integer range [1,n], where n=Max(n
1,n
2). (2) The elements are taken from either uniform distribution or non-uniform distribution such that
, for 1≤i≤p (number of processors). We give a new optimal deterministic algorithm runs in
time using p processors on EREW PRAM. For
; the running time of the algorithm is O(log (g)
n) which is faster than the previous results, where log (g)
n=log log (g−1)
n for g>1 and log (1)
n=log n. We also extend the domain of input data to [1,n
k
], where k is a constant.
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