scholarly journals A STATIC OPTIMALITY TRANSFORMATION WITH APPLICATIONS TO PLANAR POINT LOCATION

2012 ◽  
Vol 22 (04) ◽  
pp. 327-340 ◽  
Author(s):  
JOHN IACONO ◽  
WOLFGANG MULZER

Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is fine-tuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2-D analogue of the jump from Knuth's optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information.

1989 ◽  
Vol 555 (1 Combinatorial) ◽  
pp. 352-362
Author(s):  
NEIL SARNAK ◽  
ROBERT E. TARJAN

1986 ◽  
Vol 29 (7) ◽  
pp. 669-679 ◽  
Author(s):  
Neil Sarnak ◽  
Robert E. Tarjan

2021 ◽  
Vol 73 (1) ◽  
pp. 134-141
Author(s):  
A.R. Baidalina ◽  
◽  
S.A. Boranbayev ◽  

The article discusses ways of programming algorithms for complex data structures in Python. Knowledge of these structures and the corresponding algorithms is necessary when choosing the best methods for developing various software. When studying the subject "Algorithms and Data Structures", it is important to understand the essence of data structures. This is due to the fact that manipulating a data structure to fit a specific problem requires an understanding of the essence and algorithms of this data structure. Examples of programming algorithms related to dynamic lists and binary search trees in the currently widely used Python language are given. The algorithms for traversing the graph in depth and breadth are optimally and clearly implemented using the Python dictionary.


2017 ◽  
Vol 27 (01n02) ◽  
pp. 3-12
Author(s):  
Siu-Wing Cheng ◽  
Man-Kit Lau

We present a planar point location structure for a convex subdivision [Formula: see text]. Given a query sequence of length [Formula: see text], the total running time is [Formula: see text], where [Formula: see text] is the number of vertices in [Formula: see text] and [Formula: see text] is the minimum time required by any linear decision tree for answering planar point location queries in [Formula: see text] to process the same query sequence. The running time includes the preprocessing time. Therefore, for [Formula: see text], our running time is only worse than the best possible bound by [Formula: see text] per query, which is much smaller than the [Formula: see text] query time offered by a worst-case optimal planar point location structure.


1993 ◽  
Vol 5 (4) ◽  
pp. 695-704 ◽  
Author(s):  
R.P. Cheetham ◽  
B.J. Oommen ◽  
D.T.H. Ng

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