2013
DOI: 10.1016/j.comgeo.2012.11.007
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic well-spaced point sets

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2014
2014
2017
2017

Publication Types

Select...
4
1

Relationship

5
0

Authors

Journals

citations
Cited by 5 publications
(5 citation statements)
references
References 24 publications
0
5
0
Order By: Relevance
“…Variants of self-adjusting computation has been implemented in SML [1], Haskell [12], C [30], and OCaml [31]. The techniques have been applied to a number of problems in a relatively diverse set of domains including motion simulation [2,5], dynamic computational geometry [7,8], and machine learning [3,51].…”
Section: Related Workmentioning
confidence: 99%
“…Variants of self-adjusting computation has been implemented in SML [1], Haskell [12], C [30], and OCaml [31]. The techniques have been applied to a number of problems in a relatively diverse set of domains including motion simulation [2,5], dynamic computational geometry [7,8], and machine learning [3,51].…”
Section: Related Workmentioning
confidence: 99%
“…Variants of self-adjusting computation have been implemented in several host languages such as C , Java [Shankar and Bodik 2007], Haskell [Carlsson 2002], and SML [Ley-Wild et al 2008]. Self-adjusting computation often achieves asymptotically efficient updates for a reasonably broad range of benchmarks ], can help verify runtime invariants [Shankar and Bodik 2007], and even help solve major open problems in many domains including computational geometry [Acar et al 2010] and machine learning [Sümer et al 2011]. More recent work shows that the approach can be generalized to parallel computations, taking simultaneous advantage of parallelism and incremental computation time by exploiting structural similarities between them [Hammer et al 2007;Burckhardt et al 2011;, as well as large-scale distributed systems [Bhatotia et al 2011].…”
Section: Incremental and Self-adjusting Computationmentioning
confidence: 99%
“…Previous work extended existing languages including C [Hammer et al 2009 and ML Ley-Wild et al 2008] to support self-adjusting computation. Evaluations showed that the approach can achieve asymptotically efficient updates for a reasonably broad range of benchmarks , and even help solve major open problems in a range of domains including computational geometry [Acar et al 2010] and machine learning [Sümer et al 2011]. More recent work generalized the approach to support parallel computation on multicores, taking advantage of the performance benefits of parallelism and incremental computation at the same time by exploiting structural similarities between them [Burckhardt et al 2011;.…”
Section: Introductionmentioning
confidence: 99%
“…The first efficient static algorithm is due to Bern, Eppstein and Gilbert [9]. However, the first provably efficient algorithm for the dynamic problem was only recently demonstrated [4], and the kinetic version of the problem, which requires computation of quality meshes of changing set of moving objects, was open until the present work.…”
Section: Introductionmentioning
confidence: 98%
“…In self-adjusting computation, programs can respond automatically to modifications to their data by invoking a general-purpose change propagation algorithm that can also utilize a certain (traceable) data structure to ensure asymptotic efficiency [2]. These techniques have been applied effectively to other computational geometry problems such as kinetic convex hulls in 3D [3] and dynamic well-spaced point sets [4].…”
Section: Introductionmentioning
confidence: 99%