A determinantal point process is a stochastic that commonly used to capture negative correlations. It has become increasingly popular in machine learning recent years. Sampling however remains computationally intensive task. This note introduces heuristic independent particle approximation processes. The based on the physical intuition of fermions and implemented using standard numerical linear...

Given a set P of n points in the plane, the two-circle point-labeling problem consists of placing 2n uniform, non-intersecting, maximum-size open circles such that each point touches exactly two circles. It is known that this problem is NP-hard to approximate. In this paper we give a simple algorithm that improves the best previously known approximation factor from 4/(1 + √ 33) ≈ 0.5931 to 2/3....

In Ihis paper finite elemenl approximations of a one-dimensional nonlinear boundary-value problem in finite elasticity are considered. Under reasonable conditions on the form of the constitutive equations, it is shown that optimal rates of convergence can be obtained in W~.A criterion for the monolonicity of the stress operator is estahlished. In addition, L,,-estimates are also derived. A nume...

Time stamps were introduced in Shivers’s PhD thesis for approximating the result of a control-flow analysis. We show them to be suitable for computing program analyses where the space of results (e.g., control-flow graphs) is large. We formalize time-stamping as a top-down, fixed-point approximation algorithm which maintains a single copy of intermediate results. We then prove the correctness o...

In this paper, we consider an approximation of the ordering cone by a sequence of convex well-based cones that are contained inside the original cone. We derive related results on approximating minimal point sequences and propose a density result being evocative of the well-known ABB theorem.