منابع مشابه
Blocked Randomized Incremental Constructions
Randomized incremental constructions are widely used in computational geometry, but they perform very badly on large data because of their inherently random memory access patterns. We define an insertion order which removes enough randomness to significantly improve performance, but leaves enough randomness so that the algorithms remain theoretically optimal.
متن کاملQuicksort and Randomized Incremental Constructions
This is Section 5.4 in Mehlhorn/Sanders [DMS14, MS08] Quicksort is a divide-and-conquer algorithm. Let S be the set to be sorted. We select a uniformly random element p from S and split S into three parts. The set S< of elements smaller than p, the set S= of elements equal to p and the set S> of elements larger than p. The element p is usually called the pivot. Then we apply the algorithm recur...
متن کاملIncremental Markov-Model Planning
This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. The partially observable solution is incrementally constructed by considering increasing amounts of information from observations. The base solution directs the expansion of the plan by providing an evaluation function ...
متن کاملFour Results on Randomized Incremental Constructions
Raimund Seidel§ We prove four results on randomized incremental constructions (RIes): • an analysis of the expected behavior under insertion and deletions, • a fully dynamic data structure for convex hull mamtenance in arbitrary dimensions, • a tail estimate for the space complexity of RIes, • a lower bound on the complexity of agame related to RIes.
متن کاملAdaptive Incremental Mixture Markov chain Monte Carlo
We propose Adaptive Incremental Mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. Typically, adaptive MCMC methods recursively update a parametric proposal kernel with a global rule; by contrast AIMM locally adapts a non-parametric kernel. AIMM is based on an independent Metropolis-Hastings proposal d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Discrete & Computational Geometry
سال: 2009
ISSN: 0179-5376,1432-0444
DOI: 10.1007/s00454-009-9170-6