Showing results for "Computational Methods for General Sparse Matrices English"

The subject of sparse matrices has its root in such diverse fields as management science, power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of sparsity is a key to solving large problems in many fields. This second edition is a complete rewrite of the first edition published 30 years ago. Much has changed since that time. Problems have grown greatly in size and complexity; nearly all examples in the first edition were of order less than 5,000 in the ...

This volume contains the best papers presented at the 2nd ECCOMAS International Conference on Multiscale Computations for Solids and Fluids, held June 10-12, 2015.Topics dealt with include multiscale strategy for efficient development of scientific software for large-scale computations, coupled probability-nonlinear-mechanics problems and solution methods, and modern mathematical and computational setting for multi-phase flows and fluid-structure interaction.The papers cons...

This book provides a concise description of a variety of simulation methods to model soft matter with a particular focus on polymeric systems. Along with the fundamental concepts of the theory behind the methods, a comprehensive set of examples taken from the broad pool of soft materials is included. These exemplify how, thanks to the increased computational resources nowadays available to almost any research group, computational methods have become a powerful tool to sit alongside other e...

Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient ...

Matrices may seem like abstract mathematical concepts, but they play an important role in many sectors and have particularly notable applications in business. By matrices I mean matrix charts, matrix data analysis and matrix calculations, which enable efficient management and visualization of multivariate data, which is a common requirement in business activities and management practice.From strategy planning, inventory management, financial analysis to market research, matrices ha...

This second edition of a well-received book presents new perspectives on modern nanoscale problems, where fundamental science meets technology and computer modeling. Along with traditional computational techniques such as finite-difference schemes, finite element analysis and Ewald summation, the book describes a new finite-difference calculus of Flexible Local Approximation MEthods (FLAME), which qualitatively improves the numerical accuracy in a variety of problems. Application areas in ...

A textbook for a one-semester course in linear algebra for graduate or upper-level undergraduate students of mathematics and engineering. Employs a matrix perspective, and emphasizes training in definitions, theorems, and proofs. Annotation copyright Book News, Inc. Portland, Or.

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis.Features:Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically...

Computational Methods for Communication Science showcases the use of innovative computational methods in the study of communication.This book discusses the validity of using big data in communication science and showcases a number of new methods and applications in the fields of text and network analysis. Computational methods have the potential to greatly enhance the scientific study of communication because they allow us to move towards collaborative large-N studies of a...

Practical Algorithms with Python is a hands-on guide for beginner-to-intermediate developers, self-taught programmers, computer science students, data analysts, and working developers who want to improve their algorithmic problem-solving skills.The book explains practical ways to think through programming problems using data structures, search strategies, graph traversal, optimization patterns, and basic machine-learning concepts. Readers learn how to choose the right structure for...

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evoluti...

"Precision Locating and Dividing Methods" is a seminal work that delves into the intricate mathematical techniques essential for the precise segmentation and localization of various geometrical and abstract entities. Employing a rigorous analytical approach, the text combines theoretical foundations with practical applications, offering readers a comprehensive exploration of methods that span fields such as computational geometry and algorithm design. The author's choice of a didactic styl...