study guide
... Initialising the elements of an array • Arrays of variable size – Some programs, the number of entries in an array can vary – In this case, a sentinel value (for example, 9999) is used to mark the last element of the array, both in the initialising file of data items and in the array itself – Inclu ...
... Initialising the elements of an array • Arrays of variable size – Some programs, the number of entries in an array can vary – In this case, a sentinel value (for example, 9999) is used to mark the last element of the array, both in the initialising file of data items and in the array itself – Inclu ...
Theory and Practice of Monotone Minimal Perfect Hashing 1
... function (from Majewski, Wormald, Havas and Czech). We note, however, that when r is large we can use an additional bit array s to mark those vertices that have a non-zero value, and record in an array b only the (at most n) nonzero values. To compute ai , we first look at si : if it is zero, vi D 0 ...
... function (from Majewski, Wormald, Havas and Czech). We note, however, that when r is large we can use an additional bit array s to mark those vertices that have a non-zero value, and record in an array b only the (at most n) nonzero values. To compute ai , we first look at si : if it is zero, vi D 0 ...
ADFS Study Material Unit-1
... access time to the rows of the matrix is shown in Figure-9 (b). For large matrices the conservation of storage is very significant. The sequential allocation scheme for sparse matrices is also of value in that matrix operations can be executed faster than possible with a conventional two-dimensional ...
... access time to the rows of the matrix is shown in Figure-9 (b). For large matrices the conservation of storage is very significant. The sequential allocation scheme for sparse matrices is also of value in that matrix operations can be executed faster than possible with a conventional two-dimensional ...
Array Review
... May only be used on a single loop May only be used to “read” the elements of an array. May only process the array from 0 – length – 1; cannot process the array in reverse. Will process all array elements. You do not have access to the subscript. ...
... May only be used on a single loop May only be used to “read” the elements of an array. May only process the array from 0 – length – 1; cannot process the array in reverse. Will process all array elements. You do not have access to the subscript. ...
Linear-Space Data Structures for Range Frequency Queries on
... Case 1: φ is present in SL ∪ SR but not in Ssmall In this case, fφ ∈ [1, 2t], and its approximate value and approximate position (i.e., the relative position of a small block) can be encoded in O(log(t/t00 )) bits. Encoding is the same as the encoding of π in Case 1 of Dt0 (·, ·). For decoding we mo ...
... Case 1: φ is present in SL ∪ SR but not in Ssmall In this case, fφ ∈ [1, 2t], and its approximate value and approximate position (i.e., the relative position of a small block) can be encoded in O(log(t/t00 )) bits. Encoding is the same as the encoding of π in Case 1 of Dt0 (·, ·). For decoding we mo ...
Array Review
... May only be used on a single loop May only be used to “read” the elements of an array. May only process the array from 0 – length – 1; cannot process the array in reverse. Will process all array elements. You do not have access to the subscript. ...
... May only be used on a single loop May only be used to “read” the elements of an array. May only process the array from 0 – length – 1; cannot process the array in reverse. Will process all array elements. You do not have access to the subscript. ...
On A Generic Parallel Collection Framework - Infoscience
... When using multiple processors load-balancing techniques are required. In our case operations are performed on elements of the collection so dividing work can be done in straightforward way by partitioning the collection into element subsets. How partitioning is exactly done for an arbitrary collect ...
... When using multiple processors load-balancing techniques are required. In our case operations are performed on elements of the collection so dividing work can be done in straightforward way by partitioning the collection into element subsets. How partitioning is exactly done for an arbitrary collect ...
lec4
... cmp = strcmp(name, tree->name); if (cmp == 0) return treep; else if (cmp < 0) return lookup(treep->left, name); else return lookup(treep->right, name); ...
... cmp = strcmp(name, tree->name); if (cmp == 0) return treep; else if (cmp < 0) return lookup(treep->left, name); else return lookup(treep->right, name); ...
dataStruct
... // If the list is empty, set the tail reference to null if (head == null) tail = null; // The original item that was at the head of the list // no longer has anything referencing it and will be // garbage collected in Java. In other programming languages // e.g. C++, you would have to delete it othe ...
... // If the list is empty, set the tail reference to null if (head == null) tail = null; // The original item that was at the head of the list // no longer has anything referencing it and will be // garbage collected in Java. In other programming languages // e.g. C++, you would have to delete it othe ...
Concurrent Cache-Oblivious B-Trees
... that CO B-trees can outperform traditional B-trees, sometimes by factors of more than 2 [12, 19]. There are two main approaches to implementing serial CO Btrees. One approach is based on packed-memory arrays and the other on exponential search trees. Both approaches employ a static CO search tree [2 ...
... that CO B-trees can outperform traditional B-trees, sometimes by factors of more than 2 [12, 19]. There are two main approaches to implementing serial CO Btrees. One approach is based on packed-memory arrays and the other on exponential search trees. Both approaches employ a static CO search tree [2 ...
Compressed Data Structures
... Succinct rank Now we will see how to implement rank and select operations on a bit vector B[0..u). Let us start with rank-1. We will describe a data structure that consists of B itself and additional data of o(u) bits. Such a data structure using a sublinear amount of additional space is called suc ...
... Succinct rank Now we will see how to implement rank and select operations on a bit vector B[0..u). Let us start with rank-1. We will describe a data structure that consists of B itself and additional data of o(u) bits. Such a data structure using a sublinear amount of additional space is called suc ...
Succinct Orthogonal Range Search Structures on a Grid with
... We next show how to support range reporting. Lemma 4. Let N be a set of points from the universe M = [1..n] × [1..t], where n = |N | and t = O(lg n) for any constant such that 0 < < 1. If each point in N has a distinct x-coordinate, the set N can be represented using ndlg te + o(n) bits to sup ...
... We next show how to support range reporting. Lemma 4. Let N be a set of points from the universe M = [1..n] × [1..t], where n = |N | and t = O(lg n) for any constant such that 0 < < 1. If each point in N has a distinct x-coordinate, the set N can be represented using ndlg te + o(n) bits to sup ...
Linked Lists
... • We can already store collections of objects in arrays and array lists – why would we need other data structures…? • Arrays are fine, but not perfect in all respects • What do we actually do with a data structure? DCS – SWC ...
... • We can already store collections of objects in arrays and array lists – why would we need other data structures…? • Arrays are fine, but not perfect in all respects • What do we actually do with a data structure? DCS – SWC ...
Linked Lists
... • We can already store collections of objects in arrays and array lists – why would we need other data structures…? • Arrays are fine, but not perfect in all respects • What do we actually do with a data structure? RHS – SWC ...
... • We can already store collections of objects in arrays and array lists – why would we need other data structures…? • Arrays are fine, but not perfect in all respects • What do we actually do with a data structure? RHS – SWC ...
PPT - UNSW
... Java Interface for the Node List ADT (2/2) /** Inserts an element at the front of the list, returning new position. */ public void addFirst(E e); /** Inserts an element at the end of the list, returning new position. */ public void addLast(E e); /** Inserts an element after the given node in the li ...
... Java Interface for the Node List ADT (2/2) /** Inserts an element at the front of the list, returning new position. */ public void addFirst(E e); /** Inserts an element at the end of the list, returning new position. */ public void addLast(E e); /** Inserts an element after the given node in the li ...
PPT - UNSW
... Java Interface for the Node List ADT (2/2) /** Inserts an element at the front of the list, returning new position. */ public void addFirst(E e); /** Inserts an element at the end of the list, returning new position. */ public void addLast(E e); /** Inserts an element after the given node in the li ...
... Java Interface for the Node List ADT (2/2) /** Inserts an element at the front of the list, returning new position. */ public void addFirst(E e); /** Inserts an element at the end of the list, returning new position. */ public void addLast(E e); /** Inserts an element after the given node in the li ...
pptx
... (1) Only one disk can be moved at a time. (2) A disk is slid off the top of one tower onto the next tower. (3) A disk can only be placed on top of a larger disk. Write pseudocode to move the disks from the first tower to the last using stacks. ...
... (1) Only one disk can be moved at a time. (2) A disk is slid off the top of one tower onto the next tower. (3) A disk can only be placed on top of a larger disk. Write pseudocode to move the disks from the first tower to the last using stacks. ...
Slides - SRU Computer Science
... hand by bubbling to left into sorted subarray; little less brute-force than bubble sort – add one element a[i] at a time – find proper position, j+1, to the left by shifting to the ...
... hand by bubbling to left into sorted subarray; little less brute-force than bubble sort – add one element a[i] at a time – find proper position, j+1, to the left by shifting to the ...
A n
... Arrays • Array: a sequence of indexed components with the following properties: – array size is fixed at the time of array’s construction • int[] numbers = new int [10]; – array elements are placed contiguously in memory • address of any element can be calculated directly as its offset from the beg ...
... Arrays • Array: a sequence of indexed components with the following properties: – array size is fixed at the time of array’s construction • int[] numbers = new int [10]; – array elements are placed contiguously in memory • address of any element can be calculated directly as its offset from the beg ...
COMPRESSED SUFFIX ARRAYS AND SUFFIX TREES WITH
... languages (Burmese, Chinese, Taiwanese, Tibetan, etc.), which do not have a welldefined notion of terms. The set of successful search keys is possibly much larger than the set of terms in structured texts, because it consists of all feasible substrings of T ; that is, we can have as many as n2 = Θ ...
... languages (Burmese, Chinese, Taiwanese, Tibetan, etc.), which do not have a welldefined notion of terms. The set of successful search keys is possibly much larger than the set of terms in structured texts, because it consists of all feasible substrings of T ; that is, we can have as many as n2 = Θ ...
Bloom filter
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not, thus a Bloom filter has a 100% recall rate. In other words, a query returns either ""possibly in set"" or ""definitely not in set"". Elements can be added to the set, but not removed (though this can be addressed with a ""counting"" filter). The more elements that are added to the set, the larger the probability of false positives.Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if ""conventional"" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses, an 85–15 form of the Pareto principle (Bloom (1970)).More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)).