Results 291 to 300 of about 185,887 (355)
Some of the next articles are maybe not open access.
1994
For a collection of data objects, we have discussed some data organizing techniques that use array, linked list, stack, queue, tree, and graph objects (to be discussed later). Such basic operations as insertion, deletion, and even searching for these objects were discussed and implemented. A wise selection of one or more such objects for an application
Saumyendra Sengupta +1 more
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For a collection of data objects, we have discussed some data organizing techniques that use array, linked list, stack, queue, tree, and graph objects (to be discussed later). Such basic operations as insertion, deletion, and even searching for these objects were discussed and implemented. A wise selection of one or more such objects for an application
Saumyendra Sengupta +1 more
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Sorting and Searching in Multisets
SIAM Journal on Computing, 1976In this paper the problem of sorting multisets is considered. An information theoretic lower bound on the number of three branch comparisons is obtained, and it is shown that this bound is asymptotically attainable. It is shown that the multiplicities of a set can only be obtained by comparisons if the total order is discovered in the process.
Munro, Ian, Spira, Philip M.
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1992
A new neural network parallel algorithm for sorting problems is introduced in this Chapter. The proposed algorithm using 0(n2) processors requires two and only two steps, not depending on the problem size, while the conventional parallel sorting algorithm using O(n) processors proposed by Leighton needs the computation time 0(log n).
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A new neural network parallel algorithm for sorting problems is introduced in this Chapter. The proposed algorithm using 0(n2) processors requires two and only two steps, not depending on the problem size, while the conventional parallel sorting algorithm using O(n) processors proposed by Leighton needs the computation time 0(log n).
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2010
We give various sorting algorithms and some practical variants of them, like sorting index arrays and pointer sorting. Searching methods both for sorted and for unsorted arrays are described. Finally we give methods for the determination of equivalence classes.
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We give various sorting algorithms and some practical variants of them, like sorting index arrays and pointer sorting. Searching methods both for sorted and for unsorted arrays are described. Finally we give methods for the determination of equivalence classes.
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Energy Efficient Sorting, Selection and Searching
Workshop on Algorithms and Computation, 2023Varunkumar Jayapaul +3 more
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Search, Sorting, and Urban Agglomeration
Journal of Labor Economics, 2001Studies have suggested that urban agglomeration enhances productivity by facilitating the firm‐worker matching process. This article develops a model that formalizes this notion and demonstrates that, when firm capital and worker skill are complementary in production, urban agglomeration will tend to generate more efficient, yet segregated matches.
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2017
Many efficient algorithms are based on sorting the input data, because sorting often makes solving the problem easier. This chapter discusses the theory and practice of sorting as an algorithm design tool. Section 4.1 first discusses three important sorting algorithms: bubble sort, merge sort, and counting sort. After this, we will learn how to use the
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Many efficient algorithms are based on sorting the input data, because sorting often makes solving the problem easier. This chapter discusses the theory and practice of sorting as an algorithm design tool. Section 4.1 first discusses three important sorting algorithms: bubble sort, merge sort, and counting sort. After this, we will learn how to use the
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1993
Abstract In this section we will discuss some important parallel sorting algorithm. We have already seen one sorting algorithm at the end of§ 2 in chapter II. That algorithm sorted n numbers in time O(lg2n) using 0( n) processors. The theoretical lower bound in time for sorting n numbers is O(lg n) (since this many comparisons must be
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Abstract In this section we will discuss some important parallel sorting algorithm. We have already seen one sorting algorithm at the end of§ 2 in chapter II. That algorithm sorted n numbers in time O(lg2n) using 0( n) processors. The theoretical lower bound in time for sorting n numbers is O(lg n) (since this many comparisons must be
openaire +1 more source