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Spaceefficient planar convex hull algorithms
 Proc. Latin American Theoretical Informatics
, 2002
"... A spaceefficient algorithm is one in which the output is given in the same location as the input and only a small amount of additional memory is used by the algorithm. We describe four spaceefficient algorithms for computing the convex hull of a planar point set. ..."
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Cited by 20 (1 self)
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A spaceefficient algorithm is one in which the output is given in the same location as the input and only a small amount of additional memory is used by the algorithm. We describe four spaceefficient algorithms for computing the convex hull of a planar point set.
Optimal inplace planar convex hull algorithms
 Proceedings of Latin American Theoretical Informatics (LATIN 2002), volume 2286 of Lecture Notes in Computer Science
, 2002
"... An inplace algorithm is one in which the output is given in the same location as the input and only a small amount of additional memory is used by the algorithm. In this paper we describe three inplace algorithms for computing the convex hull of a planar point set. All three algorithms are optima ..."
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Cited by 5 (2 self)
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An inplace algorithm is one in which the output is given in the same location as the input and only a small amount of additional memory is used by the algorithm. In this paper we describe three inplace algorithms for computing the convex hull of a planar point set. All three algorithms are optimal, some more so than others...
SpaceEfficient Algorithms for Klee’s Measure Problem
, 2005
"... We give spaceefficient geometric algorithms for three related problems. Given a set of n axisaligned rectangles in the plane, we calculate the area covered by the union of these rectangles (Klee’s measure problem) in O(n 3/2 log n) time with O(√n) extra space. If the input can be destroyed and the ..."
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Cited by 5 (0 self)
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We give spaceefficient geometric algorithms for three related problems. Given a set of n axisaligned rectangles in the plane, we calculate the area covered by the union of these rectangles (Klee’s measure problem) in O(n 3/2 log n) time with O(√n) extra space. If the input can be destroyed and there are no degenerate cases and input coordinates are all integers, we can solve Klee’s measure problem in O(n log² n) time with O(log² n) extra space. Given a set of n points in the plane, we find the axisaligned unit square that covers the maximum number of points in O(n log³ n) time with O(log² n) extra space.
An InPlace Sorting Algorithm Performing O(n log n) Comparisons and . . .
, 2003
"... In this paper we give a positive answer to the longstanding problem of finding an inplace sorting algorithm performing O(n log n) comparisons and O(n) data moves in the worst case. So far, the better inplace sorting algorithm with O(n) moves was proposed by Munro and V. Raman. Their algorithm ..."
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Cited by 5 (2 self)
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In this paper we give a positive answer to the longstanding problem of finding an inplace sorting algorithm performing O(n log n) comparisons and O(n) data moves in the worst case. So far, the better inplace sorting algorithm with O(n) moves was proposed by Munro and V. Raman. Their algorithm requires O(n ) comparisons in the worst case, for any # > 0. Later,
ComparisonBased Time–Space Lower Bounds for Selection
"... We establish the first nontrivial lower bounds on timespace tradeoffs for the selection problem. We prove that any comparisonbased randomized algorithm for finding the median requires Ω(n log logS n) expected time in the RAM model (or more generally in the comparison branching program model), if we ..."
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Cited by 5 (1 self)
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We establish the first nontrivial lower bounds on timespace tradeoffs for the selection problem. We prove that any comparisonbased randomized algorithm for finding the median requires Ω(n log logS n) expected time in the RAM model (or more generally in the comparison branching program model), if we have S bits of extra space besides the readonly input array. This bound is tight for all S ≫ log n, and remains true even if the array is given in a random order. Our result thus answers a 16yearold question of Munro and Raman, and also complements recent lower bounds that are restricted to sequential access, as in the multipass streaming model [Chakrabarti et al., SODA 2008]. We also prove that any comparisonbased, deterministic, multipass streaming algorithm for finding the median requires Ω(n log ∗ (n/s) + n log s n) worstcase time (in scanning plus comparisons), if we have s cells of space. This bound is also tight for all s ≫ log 2 n. We get deterministic lower bounds for I/Oefficient algorithms as well. All proofs in this paper involve “elementary ” techniques only. 1
The Ultimate Heapsort
 In Proceedings of the Computing: the 4th Australasian Theory Symposium, Australian Computer Science Communications
, 1998
"... . A variant of Heapsortnamed Ultimate Heapsortis presented that sorts n elements inplace in \Theta(n log 2 (n+ 1)) worstcase time by performing at most n log 2 n + \Theta(n) key comparisons and n log 2 n + \Theta(n) element moves. The secret behind Ultimate Heapsort is that it occasionally ..."
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Cited by 4 (0 self)
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. A variant of Heapsortnamed Ultimate Heapsortis presented that sorts n elements inplace in \Theta(n log 2 (n+ 1)) worstcase time by performing at most n log 2 n + \Theta(n) key comparisons and n log 2 n + \Theta(n) element moves. The secret behind Ultimate Heapsort is that it occasionally transforms the heap it operates with to a twolayer heap which keeps small elements at the leaves. Basically, Ultimate Heapsort is like BottomUp Heapsort but, due to the twolayer heap property, an element taken from a leaf has to be moved towards the root only O(1) levels, on an average. Let a[1::n] be an array of n elements each consisting of a key and some information associated with this key. This array is a (maximum) heap if, for all i 2 f2; : : : ; ng, the key of element a[bi=2c] is larger than or equal to that of element a[i]. That is, a heap is a pointerfree representation of a left complete binary tree, where the elements stored are partially ordered according to their keys. Ele...
Distributionsensitive set multipartitioning
 1st International Conference on the Analysis of Algorithms
, 2005
"... Given a set S with realvalued members, associated with each member one of two possible types; a multipartitioning of S is a sequence of the members of S such that if x, y ∈ S have different types and x < y, x precedes y in the multipartitioning of S. We give two distributionsensitive algorithms ..."
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Cited by 3 (2 self)
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Given a set S with realvalued members, associated with each member one of two possible types; a multipartitioning of S is a sequence of the members of S such that if x, y ∈ S have different types and x < y, x precedes y in the multipartitioning of S. We give two distributionsensitive algorithms for the set multipartitioning problem and a matching lower bound in the algebraic decisiontree model. One of the two algorithms can be made stable and can be implemented in place. We also give an outputsensitive algorithm for the problem.
InPlace Sorting With Fewer Moves
 Information Processing Letters
, 1999
"... It is shown that an array of n elements can be sorted using O(1) extra space, O(n log n= log log n) element moves, and n log 2 n+O(n log log n) comparisons. This is the first inplace sorting algorithm requiring o(n log n) moves in the worst case while guaranteeing O(n log n) comparisons but, due to ..."
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It is shown that an array of n elements can be sorted using O(1) extra space, O(n log n= log log n) element moves, and n log 2 n+O(n log log n) comparisons. This is the first inplace sorting algorithm requiring o(n log n) moves in the worst case while guaranteeing O(n log n) comparisons but, due to the constant factors involved, the algorithm is predominantly of theoretical interest. Key words: Inplace algorithms, sorting, merging, mergesort, multiway merge 1