An official website of the United States government
Abstract Consider two half-spaces$$H_1^+$$ and$$H_2^+$$ in$${\mathbb {R}}^{d+1}$$ whose bounding hyperplanes$$H_1$$ and$$H_2$$ are orthogonal and pass through the origin. The intersection$${\mathbb {S}}_{2,+}^d:={\mathbb {S}}^d\cap H_1^+\cap H_2^+$$ is a spherical convex subset of thed-dimensional unit sphere$${\mathbb {S}}^d$$ , which contains a great subsphere of dimension$$d-2$$ and is called a spherical wedge. Choosenindependent random points uniformly at random on$${\mathbb {S}}_{2,+}^d$$ and consider the expected facet number of the spherical convex hull of these points. It is shown that, up to terms of lower order, this expectation grows like a constant multiple of$$\log n$$ . A similar behaviour is obtained for the expected facet number of a homogeneous Poisson point process on$${\mathbb {S}}_{2,+}^d$$ . The result is compared to the corresponding behaviour of classical Euclidean random polytopes and of spherical random polytopes on a half-sphere.
more »
« less
Balanced Allocation Through Random Walk
We consider the allocation problem in which $$m \leq (1-\epsilon) dn $$ items are to be allocated to $$n$$ bins with capacity $$d$$. The items $$x_1,x_2,\ldots,x_m$$ arrive sequentially and when item $$x_i$$ arrives it is given two possible bin locations $$p_i=h_1(x_i),q_i=h_2(x_i)$$ via hash functions $$h_1,h_2$$. We consider a random walk procedure for inserting items and show that the expected time insertion time is constant provided $$\epsilon = \Omega\left(\sqrt{ \frac{ \log d}{d}} \right).$$
more »
« less
- Award ID(s):
- 1661063
- PAR ID:
- 10054150
- Date Published:
- Journal Name:
- Information processing letters
- Volume:
- 131
- ISSN:
- 1872-6119
- Page Range / eLocation ID:
- 39-43
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Kernelized Gram matrix $$W$$ constructed from data points $$\{x_i\}_{i=1}^N$$ as $$W_{ij}= k_0( \frac{ \| x_i - x_j \|^2} {\sigma ^2} ) $$ is widely used in graph-based geometric data analysis and unsupervised learning. An important question is how to choose the kernel bandwidth $$\sigma $$, and a common practice called self-tuned kernel adaptively sets a $$\sigma _i$$ at each point $$x_i$$ by the $$k$$-nearest neighbor (kNN) distance. When $$x_i$$s are sampled from a $$d$$-dimensional manifold embedded in a possibly high-dimensional space, unlike with fixed-bandwidth kernels, theoretical results of graph Laplacian convergence with self-tuned kernels have been incomplete. This paper proves the convergence of graph Laplacian operator $$L_N$$ to manifold (weighted-)Laplacian for a new family of kNN self-tuned kernels $$W^{(\alpha )}_{ij} = k_0( \frac{ \| x_i - x_j \|^2}{ \epsilon \hat{\rho }(x_i) \hat{\rho }(x_j)})/\hat{\rho }(x_i)^\alpha \hat{\rho }(x_j)^\alpha $$, where $$\hat{\rho }$$ is the estimated bandwidth function by kNN and the limiting operator is also parametrized by $$\alpha $$. When $$\alpha = 1$$, the limiting operator is the weighted manifold Laplacian $$\varDelta _p$$. Specifically, we prove the point-wise convergence of $$L_N f $$ and convergence of the graph Dirichlet form with rates. Our analysis is based on first establishing a $C^0$ consistency for $$\hat{\rho }$$ which bounds the relative estimation error $$|\hat{\rho } - \bar{\rho }|/\bar{\rho }$$ uniformly with high probability, where $$\bar{\rho } = p^{-1/d}$$ and $$p$$ is the data density function. Our theoretical results reveal the advantage of the self-tuned kernel over the fixed-bandwidth kernel via smaller variance error in low-density regions. In the algorithm, no prior knowledge of $$d$$ or data density is needed. The theoretical results are supported by numerical experiments on simulated data and hand-written digit image data.more » « less
-
Abstract When k and s are natural numbers and $${\mathbf h}\in {\mathbb Z}^k$$, denote by $$J_{s,k}(X;\,{\mathbf h})$$ the number of integral solutions of the system $$ \sum_{i=1}^s(x_i^j-y_i^j)=h_j\quad (1\leqslant j\leqslant k), $$ with $$1\leqslant x_i,y_i\leqslant X$$. When $$s\lt k(k+1)/2$$ and $$(h_1,\ldots ,h_{k-1})\ne {\mathbf 0}$$, Brandes and Hughes have shown that $$J_{s,k}(X;\,{\mathbf h})=o(X^s)$$. In this paper we improve on quantitative aspects of this result, and, subject to an extension of the main conjecture in Vinogradov’s mean value theorem, we obtain an asymptotic formula for $$J_{s,k}(X;\,{\mathbf h})$$ in the critical case $s=k(k+1)/2$. The latter requires minor arc estimates going beyond square-root cancellation.more » « less
-
We consider the problem of performing linear regression over a stream of d-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples (a_1,b_1), (a_2,b_2)..., with a_i drawn independently from a d-dimensional isotropic Gaussian, and where b_i = + \eta_i, for a fixed x in R^d with ||x||= 1 and with independent noise \eta_i drawn uniformly from the interval [-2^{-d/5},2^{-d/5}]. We show that any algorithm with at most d^2/4 bits of memory requires at least \Omega(d \log \log \frac{1}{\epsilon}) samples to approximate x to \ell_2 error \epsilon with probability of success at least 2/3, for \epsilon sufficiently small as a function of d. In contrast, for such \epsilon, x can be recovered to error \epsilon with probability 1-o(1) with memory O\left(d^2 \log(1/\epsilon)\right) using d examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization.more » « less
-
We consider the task of assigning indivisible goods to a set of agents in a fair manner. Our notion of fairness is Nash social welfare, i.e., the goal is to maximize the geometric mean of the utilities of the agents. Each good comes in multiple items or copies, and the utility of an agent diminishes as it receives more items of the same good. The utility of a bundle of items for an agent is the sum of the utilities of the items in the bundle. Each agent has a utility cap beyond which he does not value additional items. We give a polynomial time approximation algorithm that maximizes Nash social welfare up to a factor of e^{1/{e}} ~ 1.445. The computed allocation is Pareto-optimal and approximates envy-freeness up to one item up to a factor of 2 + epsilon.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
