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Ivan Stanimirovi? Algorithms & Data Structures (Hardback)
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Specificaties
- Objectstaat
- Book Title
- Algorithms & Data Structures
- Title
- Algorithms & Data Structures
- Contributor
- Ivan Stanimirovi? (Edited by)
- EAN
- 9781680944631
- ISBN
- 9781680944631
- Genre
- Computing & Internet
- Release Date
- 30/11/2016
- Release Year
- 2016
- Country/Region of Manufacture
- CA
- Item Length
- 152mm
- Publication Year
- 2016
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Publication Name
- Algorithms & Data Structures
- Item Height
- 229mm
- Publisher
- Arcler Education INC International Concepts
- Item Width
- 152mm
- Subject
- Computer Science
- Number of Pages
- 276 Pages
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Product Information
Asymptotic algorithm analysis is a methodology which has been given a lot of attention recently. Several methods of asymptotic analysis are considered to estimate the resource consumption of an algorithm, giving an assessment if a proposed algorithm can meet the resource constraints for a problem before the implementation. Processing nodes of the binary and non-binary trees in an organized manner is investigated using various algorithms. Several methods for implementing binary trees and their nodes are given. Issues relating to the design of algorithms and data structures for disk-based applications are solved, as well as problems of searching data stored in lists and tables. Algorithms for solving some problems related to finding shortest routes in a graph and the minimum-cost spanning tree, are applied to determine lowest-cost connectivity in a network. The initial five chapters of this book considers asymptotic algorithm analysis and provide various algorithms, such as modification of LMS algorithm, a direct search algorithm is proposed for minimizing an arbitrary function, etc. The following nine chapters present generative algorithms for random graphs, trees and big data. The remaining content of this book focuses on the advances of specific methods and algorithms in the field of data structures, especially in graph theory. The mean square convergence of the LMS algorithm is investigated for the large class of linearly filtered random driving processes, containing the following contributions: (i) The parameter error vector covariance matrix can be decomposed into two parts, (ii) The impact of additive noise is shown to contribute only to the modal space of the driving process independently from the noise statistic and thus defines the steady state of the filter. The certain and uncertain neutral systems with time-delay and saturating actuator are considered. In order to analyse and optimize the system, auxiliary functions are presented based on additive decomposition approach and the relationship among them is discussed. As the novel stability criterion, two sufficient conditions are obtained for asymptotic stability of the neutral systems. Furthermore, the stability analysis algorithm and optimality algorithm are introduced to optimize the result. A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm's performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. In the pursuit of finding subclasses of the makespan minimization problem on unrelated parallel machines that have approximation algorithms with approximation ratio better than 2, the graph balancing problem has been of current interest. In the graph balancing problem each job can be non-preemptively scheduled on one of at most two machines with the same processing time on either machine. A 3/2 -approximation algorithm for the graph balancing problem is presented. Recently manifold learning has received extensive interest in the community of pattern recognition. Despite their appealing properties, most manifold learning algorithms are not robust in practical applications. This problem is addressed in the context of the Hessian locally linear embedding (HLLE) algorithm and propose a more robust method, called RHLLE, which aims to be robust against both outliers and noise in the data. Specifically, a fast outlier detection method for high-dimensional datasets is proposed. Then, a local smoothing method is employed to reduce noise. Nowadays, a leading instance of
Product Identifiers
Publisher
Arcler Education INC International Concepts
ISBN-13
9781680944631
eBay Product ID (ePID)
23046729950
Product Key Features
Publication Name
Algorithms & Data Structures
Format
Hardcover
Language
English
Subject
Computer Science
Publication Year
2016
Type
Textbook
Number of Pages
276 Pages
Dimensions
Item Height
229mm
Item Width
152mm
Additional Product Features
Country/Region of Manufacture
Canada
Editor
Ivan Stanimirovi?
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