Learn IT - Books tags linear

Derived Langlands: Monomial Resolutions of Admissible Representations (Series on Number Theory and Its Applications)

World Scientific Publishing, 2019

The Langlands Programme is one of the most important areas in modern pure mathematics. The importance of this volume lies in its potential to recast many aspects of the programme in an entirely new context. For example, the morphisms in the monomial category of a locally p–adic Lie group have a distributional description, due...

A Computational Introduction to Number Theory and Algebra

Cambridge University Press, 2005

"This is an outstanding and well-written book whose aim is to introduce the reader to a broad range of material -- ranging from basic to relatively advanced -- without requiring any prior knowledge on the part of the reader other than calculus and mathematical maturity. That the book succeeds at this goal is quite an accomplishment! ...this...

MATLAB Linear Algebra

Apress, 2014

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or...

Computer Algebra Recipes: An Advanced Guide to Scientific Modeling

Springer, 2007

A computer algebra system (CAS) not only has the number \crunching" and plotting capability of traditional computing languages such as Fortran and C, but also allows one to perform the symbolic manipulations and derivations required in most mathematically based science and engineering courses. To introduce students in these disciplines to...

Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)

Springer, 2012

Polynomial optimization, as its name suggests, is used to optimize a generic multivariate polynomial function, subject to some suitable polynomial equality and/or inequality constraints. Such problem formulation dates back to the nineteenth century when the relationship between nonnegative polynomials and sum of squares (SOS) was...

MATLAB Matrix Algebra (Matlab Solutions)

Apress, 2014

Learning Cassandra for Administrators

Packt Publishing, 2013

Understand the immense capabilities of Cassandra in managing large amounts of data and learn how to ensure that data is always available. This practical, hands-on guide takes you through every stage from installation to performance tuning.

Overview

Install and set up a multi datacenter Cassandra

...

Problems on Partial Differential Equations (Problem Books in Mathematics)

Springer, 2019

This book covers a diverse range of topics in Mathematical Physics, linear and nonlinear PDEs. Though the text reflects the classical theory, the main emphasis is on introducing readers to the latest developments based on the notions of weak solutions and Sobolev spaces.

In numerous problems, the student is asked to...

MATLAB Optimization Techniques

Apress, 2014

Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing

John Wiley & Sons, 2008

A thorough guide to the classical and contemporary mathematical methods of modern signal and image processing

Discrete Fourier Analysis and Wavelets presents a thorough introduction to the mathematical foundations of signal and image processing. Key concepts and applications are addressed in a thought-provoking...

Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

O'Reilly, 2015

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as...

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Packt Publishing, 2018

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

Explore statistics and complex mathematics for data-intensive applications

Discover new developments in EM algorithm, PCA, and bayesian...

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