Learn IT - Books tags algebra

Problem Solving with C++

Addison Wesley, 2008

This book is meant to be used in a first course in programming and computer science using the C++ language. It assumes no previous programming experience and no mathematics beyond high school algebra.

If you have used the previous edition of this book, you should read the following section that explains the changes...

Solving Nonlinear Partial Differential Equations with Maple and Mathematica

Springer, 2014

The emphasis of the book is given in how to construct different types of solutions (exact, approximate analytical, numerical, graphical) of numerous nonlinear PDEs correctly, easily, and quickly. The reader can learn a wide variety of techniques and solve numerous nonlinear PDEs included and many other differential equations, simplifying...

Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra (Undergraduate Texts in Mathematics)

Springer, 2008

This book details the heart and soul of modern commutative and algebraic geometry. It covers such topics as the Hilbert Basis Theorem, the Nullstellensatz, invariant theory, projective geometry, and dimension theory. In addition to enhancing the text of the second edition, with over 200 pages reflecting changes to enhance clarity and...

Python Data Analysis

Packt Publishing, 2017

Key Features

Find, manipulate, and analyze your data using the Python 3.5 libraries

Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code

An easy-to-follow guide with realistic examples that are frequently used in real-world data...

Differential Equations: An Introduction with Mathematica® (Undergraduate Texts in Mathematics)

Springer, 2010

The first edition (94301-3) was published in 1995 in TIMS and had 2264 regular US sales, 928 IC, and 679 bulk.

This new edition updates the text to Mathematica 5.0 and offers a more extensive treatment of linear algebra. It has been thoroughly revised and corrected throughout.

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C++ Toolkit for Engineers and Scientists

Springer, 1999

This concise guide covers the fundamental aspects of the numerical analysis, basing upon it the construction of its routines for solving nonlinear equations, linear and nonlinear systems of equations, and eigenvalue problems. Focusing on software development, this book emphasizes software tools, OOP techniques for handling vectors, polynomials,...

Algorithms in Combinatorial Design Theory (Mathematics Studies)

Elsevier Limited, 1985

Recent years have seen an explosive growth in research in combinatorics and graph theory. One primary factor in this rapid development has been the advent of computers, and the parallel study of practical and efficient algorithms. This volume represents an attempt to...

Spectral Methods in MATLAB (Software, Environments, Tools)

SIAM, 2001

This is the only book on spectral methods built around MATLAB programs. Along with finite differences and finite elements, spectral methods are one of the three main technologies for solving partial differential equations on computers. Since spectral methods involve significant linear algebra and graphics they are very suitable for the high...

Introduction to Probability and Statistics Using R

K. G. Saur, 2019

This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three... More > semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors....

Statistical Computing in C++ and R (Chapman & Hall/CRC The R Series)

CRC Press, 2011

With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather...

Foundational and Applied Statistics for Biologists Using R

CRC Press, 2013

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students...

Mathematical Methods and Algorithms for Signal Processing

Pearson Education, 1999

Mathematical Methods and Algorithms for Signal Processing tackles the challenge of providing readers and practitioners with the broad tools of mathematics employed in modern signal processing. Building from an assumed background in signals and stochastic processes, the book provides a solid foundation in analysis, linear...

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