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Bounding overwatch
About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.
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Patent•
23 Jan 2008
TL;DR: In this paper, the authors present methods, systems and computer program code (software) products for terminating spatial partition hierarchies and other hierarchies by a priori bounding, thereby to provide, among other aspects, more efficient ray tracing in computer graphics systems.
Abstract: The present invention provides methods, systems and computer program code (software) products for terminating spatial partition hierarchies and other hierarchies by a priori bounding, thereby to provide, among other aspects, more efficient ray tracing in computer graphics systems.
66 citations
TL;DR: A program called POINCARE is described that analyzes one-parameter autonomous planar systems at the level of experts through a combination of theoretical dynamics, numerical simulation, and geometric reasoning.
62 citations
TL;DR: It is proved that a recently introduced Markov kernel can inherit the properties of variance bounding and geometric ergodicity from its intractable Metropolis–Hastings counterpart, under reasonably weak conditions.
Abstract: Approximate Bayesian computation has emerged as a standard computational tool when dealing with intractable likelihood functions in Bayesian inference. We show that many common Markov chain Monte Carlo kernels used to facilitate inference in this setting can fail to be variance bounding and hence geometrically ergodic, which can have consequences for the reliability of estimates in practice. This phenomenon is typically independent of the choice of tolerance in the approximation. We prove that a recently introduced Markov kernel can inherit the properties of variance bounding and geometric ergodicity from its intractable Metropolis–Hastings counterpart, under reasonably weak conditions. We show that the computational cost of this alternative kernel is bounded whenever the prior is proper, and present indicative results for an example where spectral gaps and asymptotic variances can be computed, as well as an example involving inference for a partially and discretely observed, time-homogeneous, pure jump Markov process. We also supply two general theorems, one providing a simple sufficient condition for lack of variance bounding for reversible kernels and the other providing a positive result concerning inheritance of variance bounding and geometric ergodicity for mixtures of reversible kernels.
62 citations
TL;DR: Predictive bounding control is introduced, which contrasts with existing self-tuning control methods which base control synthesis on a nominal plant model and permits detection of abrupt plant changes and adaptive tracking of time-varying plant characteristics.
61 citations
TL;DR: An improved algorithm for finding exact solutions to Max-Cut and the related binary quadratic programming problem, both classic problems of combinatorial optimization is presented, and extensive experiments show that the algorithm dominates the best existing method.
Abstract: We present an improved algorithm for finding exact solutions to Max-Cut and the related binary quadratic programming problem, both classic problems of combinatorial optimization. The algorithm uses a branch-(and-cut-)and-bound paradigm, using standard valid inequalities and nonstandard semidefinite bounds. More specifically, we add a quadratic regularization term to the strengthened semidefinite relaxation in order to use a quasi-Newton method to compute the bounds. The ratio of the tightness of the bounds to the time required to compute them can be controlled by two real parameters; we show how adjusting these parameters and the set of strengthening inequalities gives us a very efficient bounding procedure. Embedding our bounding procedure in a generic branch-and-bound platform, we get a competitive algorithm: extensive experiments show that our algorithm dominates the best existing method.
61 citations