Jose M Garrido

Introduction to Computational Modeling Using C and Open-Source Tools

• Broschiertes Buch

Produktbeschreibung

This book presents the fundamental principles of computational models from a computer science perspective and explains how to implement the models using the C programming language. Emphasizing analytical skill development and problem solving, the book helps readers understand how to reason about and conceptualize the problems, generate mathematical formulations, and computationally visualize and solve the problems. It provides the foundation to understand more advanced scientific computing, including parallel computing using MPI, grid computing, and other techniques in high-performance computing.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 464
• Erscheinungstermin: 19. Juni 2019
• Englisch
• Abmessung: 234mm x 155mm x 25mm
• Gewicht: 635g
• ISBN-13: 9780367379230
• ISBN-10: 0367379236
• Artikelnr.: 57781573

Autorenporträt

José M. Garrido is professor in the Department of Computer Science at Kennesaw State University. Dr. Garrido's research focuses on object-oriented modeling and simulation, multidisciplinary computational modeling, formal specification of real-time systems, language design and processors, and modeling systems performance. He developed the Psim3, PsimJ, and PsimJ2 simulation packages for C++ and Java as well as the Object-Oriented Simulation Language (OOSimL). He received a Ph.D. from George Mason University.

Inhaltsangabe

Problem Solving and Computing. Programs. Modular Decomposition: Functions.
More Concepts of Computational Models. Algorithms and Programs. Selection.
Repetition. Arrays. Pointers. Linked Lists. Text Data. Computational Models
with Arithmetic Growth. Computational Models with Quadratic Growth. Models
with Geometric Growth. Computational Models with Polynomials. Models with
Interpolation and Curve Fitting. Using Vectors with the GSL. Matrices and
Sets of Linear Equations. Introduction to Dynamical Systems. Linear
Optimization Modeling. Solving Linear Optimization Problems. Sensitivity
Analysis and Duality. Transportation Models. Network Models. Integer Linear
Optimization Models. Appendices.