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|Schaum's Outline of Matrix Operations (Schaum's Outlines)
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Author: Richard Bronson
The ideal review for your matrix operations course
More than 40 million students have trusted Schaum’s Outlines for their expert knowledge and helpful solved problems. Written by renowned experts in their respective fields, Schaum’s Outlines cover everything from math to science, nursing to language. The main feature for all these books is the solved problems. Step-by-step, authors walk readers through coming up with solutions to exercises in their topic of choice.
- 363 detailed problems with step-by-step solutions
- Clear, concise explanations of matrix operations
- Help with Eigenvalues and the QR Algorithm
- A solved-problem approach that teaches you with hands-on help
- Exercises for improving your problem-solving skills
- Matix Operations
- Richard Bronson
|Partially Specified Matrices and Operators: Classification, Completion, Applications (Operator Theory: Advances and Applications)
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Author: Israel Gohberg
Brand: Brand: Birkhäuser Basel
This book is devoted to a new direction in linear algebra and operator theory that deals with the invariants of partially specified matrices and operators, and with the spectral analysis of their completions. The theory developed centers around two major problems concerning matrices of which part of the entries are given and the others are unspecified. The first is a classification problem and aims at a simplification of the given part with the help of admissible similarities. The results here may be seen as a far reaching generalization of the Jordan canonical form. The second problem is called the eigenvalue completion problem and asks to describe all possible eigenvalues and their multiplicities of the matrices which one obtains by filling in the unspecified entries. Both problems are also considered in an infinite dimensional operator framework. A large part of the book deals with applications to matrix theory and analysis, namely to stabilization problems in mathematical system theory, to problems of Wiener-Hopf factorization and interpolation for matrix polynomials and rational matrix functions, to the Kronecker structure theory of linear pencils, and to non everywhere defined operators. The eigenvalue completion problem has a natural associated inverse, which appears as a restriction problem. The analysis of these two problems is often simpler when a solution of the corresponding classification problem is available.
- Used Book in Good Condition
|Linear Models and the Relevant Distributions and Matrix Algebra (Chapman & Hall/CRC Texts in Statistical Science)
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Author: David A. Harville
Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models, or a resource used (in a course on mathematical statistics) to illustrate various theoretical concepts in the context of a relatively complex setting of great practical importance.
- Provides coverage of matrix algebra that is extensive and relatively self-contained and does so in a meaningful context
- Provides thorough coverage of the relevant statistical distributions, including spherically and elliptically symmetric distributions
- Includes extensive coverage of multiple-comparison procedures (and of simultaneous confidence intervals), including procedures for controlling the k-FWER and the FDR
- Provides thorough coverage (complete with detailed and highly accessible proofs) of results on the properties of various linear-model procedures, including those of least squares estimators and those of the F test.
- Features the use of real data sets for illustrative purposes
- Includes many exercises
David Harville served for 10 years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories at Wright-Patterson AFB, Ohio, 20 years as a full professor in Iowa State University’s Department of Statistics where he now has emeritus status, and seven years as a research staff member of the Mathematical Sciences Department of IBM’s T.J. Watson Research Center. He has considerable relevant experience, having taught M.S. and Ph.D. level courses in linear models, been the thesis advisor of 10 Ph.D. graduates, and authored or co-authored two books and more than 80 research articles. His work has been recognized through his election as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and as a member of the International Statistical Institute.
|Python for beginners: Step-By-Step Guide to Learning Python Programming
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Author: Larry Lutz
PYTHON FOR BEGINNERS
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Python language is widely used all over the globe. Its popularity is because of its characteristics and many advantages attached to it. Some of the major advantages are as follows:
- Easy-to-Learn, Read and Maintain
- A handful of Standard Libraries
- Easy development and Test
- Extendable to Low-Level languages
This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning.
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- Introduction to Python
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- Lists and Tuples
- Control Statements
- Functions and Modules
- File Input - Output
- Object-oriented Programming
- Code optimization
- Useful python libraries
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|Stochastic Processes and Random Matrices: Lecture Notes of the Les Houches Summer School: Volume 104, July 2015
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The field of stochastic processes and Random Matrix Theory (RMT) has been a rapidly evolving subject during the last fifteen years. The continuous development and discovery of new tools, connections and ideas have led to an avalanche of new results. These breakthroughs have been made possible thanks, to a large extent, to the recent development of various new techniques in RMT.
Matrix models have been playing an important role in theoretical physics for a long time and they are currently also a very active domain of research in mathematics. An emblematic example of these recent advances concerns the theory of growth phenomena in the Kardar-Parisi-Zhang (KPZ) universality class where the joint efforts of physicists and mathematicians during the last twenty years have unveiled the beautiful connections between this fundamental problem of statistical mechanics and the theory of random matrices, namely the fluctuations of the largest eigenvalue of certain ensembles of random matrices.
This text not only covers this topic in detail but also presents more recent developments that have emerged from these discoveries, for instance in the context of low dimensional heat transport (on the physics side) or integrable probability (on the mathematical side).
|Python: Programming: Your Step By Step Guide To Easily Learn Python in 7 Days (Python for Beginners, Python Programming for Beginners, Learn Python, Python Language)
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Author: iCode Academy
Are You Ready To Learn Python Easily? Learning Python Programming in 7 days is possible, although it might not look like it’s easy at first, especially if you are someone with meager experience in dealing with a programming language. As you learn, you will see that it is not that difficult to understand, and it becomes easier when you add patience, diligence, and discipline. This book has all the materials that you need in learning basic Python Programming. It is presented in easy to understand format – much of technical jargons were eliminated, although you might still find some, but they are the terms that you will likely (almost always) encounter when creating your codes. You will find no such trouble in understanding the terms, and how to do it properly. Samples are provided for you, and explanations are also presented so you won’t get lost and still have fun while learning. You will be glad that you have this book with you while learning Python. Discover many things that you can do with Python programming, and you might even start a new career.
Here’s What You’ll Learn From This Python For Beginners Book: ✓ Introduction ✓ Chapter 1: Welcome to the World of the Python ✓ Chapter 2: Python Syntax ✓ Chapter 3: Important Strings and Console Output ✓ Chapter 4: The Conditionals and Control Flow ✓ Chapter 5: Understanding Lists, Tuples, and Dictionaries ✓ Chapter 6: The Loops ✓ Chapter 7: Understanding the Functions ✓ Chapter 8: Coding a Full Program ✓ Chapter 9: Reserved Words and Functions ✓ and much more
What Are You Waiting For?
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|Iterative Methods for Sparse Linear Systems, Second Edition
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Author: Yousef Saad
Brand: Brand: Society for Industrial and Applied Mathematics
Tremendous progress has been made in the scientific and engineering disciplines regarding the use of iterative methods for linear systems. The size and complexity of linear and nonlinear systems arising in typical applications has grown, meaning that using direct solvers for the three-dimensional models of these problems is no longer effective. At the same time, parallel computing, becoming less expensive and standardized, has penetrated these application areas. Iterative methods are easier than direct solvers to implement on parallel computers but require approaches and solution algorithms that are different from classical methods. This second edition gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations, including a wide range of the best methods available today. A new chapter on multigrid techniques has been added, whilst material throughout has been updated, removed or shortened. Numerous exercises have been added, as well as an updated and expanded bibliography.
- Used Book in Good Condition
|Applied Linear Algebra and Matrix Analysis (Undergraduate Texts in Mathematics)
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Author: Thomas S. Shores
This new book offers a fresh approach to matrix and linear algebra by providing a balanced blend of applications, theory, and computation, while highlighting their interdependence. Intended for a one-semester course, Applied Linear Algebra and Matrix Analysis places special emphasis on linear algebra as an experimental science, with numerous examples, computer exercises, and projects. While the flavor is heavily computational and experimental, the text is independent of specific hardware or software platforms. Throughout the book, significant motivating examples are woven into the text, and each section ends with a set of exercises.
|Topics in Random Matrix Theory (Graduate Studies in Mathematics)
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Author: Terence Tao
The field of random matrix theory has seen an explosion of activity in recent years, with connections to many areas of mathematics and physics. However, this makes the current state of the field almost too large to survey in a single book. In this graduate text, we focus on one specific sector of the field, namely the spectral distribution of random Wigner matrix ensembles (such as the Gaussian Unitary Ensemble), as well as iid matrix ensembles. The text is largely self-contained and starts with a review of relevant aspects of probability theory and linear algebra. With over 200 exercises, the book is suitable as an introductory text for beginning graduate students seeking to enter the field.
|Matrices and Linear Transformations: Second Edition (Dover Books on Mathematics)
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Author: Charles G. Cullen
"Comprehensive . . . an excellent introduction to the subject." — Electronic Engineer's Design Magazine.
This introductory textbook, aimed at sophomore- and junior-level undergraduates in mathematics, engineering, and the physical sciences, offers a smooth, in-depth treatment of linear algebra and matrix theory. The major objects of study are matrices over an arbitrary field.
Contents include Matrices and Linear Systems; Vector Spaces; Determinants; Linear Transformations; Similarity: Part I and Part II; Polynomials and Polynomial Matrices; Matrix Analysis; and Numerical Methods.
The first seven chapters, which require only a first course in calculus and analytic geometry, deal with matrices and linear systems, vector spaces, determinants, linear transformations, similarity, polynomials, and polynomial matrices. Chapters 8 and 9, parts of which require the student to have completed the normal course sequence in calculus and differential equations, provide introductions to matrix analysis and numerical linear algebra, respectively. Among the key features are coverage of spectral decomposition, the Jordan canonical form, the solution of the matrix equation AX = XB, and over 375 problems, many with answers.
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