**Set Linear Algebra and Set Fuzzy Linear Algebra**

by W. B. V. Kandasamy, F. Smarandache, K. Ilanthenral

**Publisher**: InfoLearnQuest 2008**ISBN/ASIN**: 1599730294**ISBN-13**: 9781599730295**Number of pages**: 345

**Description**:

Set linear algebras, introduced by the authors in this book, are the most generalized form of linear algebras. These structures make use of very few algebraic operations and are easily accessible to non-mathematicians as well. The dominance of computers in everyday life calls for a paradigm shift in the concepts of linear algebra. The authors believe that set linear algebra will cater to that need.

Download or read it online for free here:

**Download link**

(2.5MB, PDF)

## Similar books

**Linear Algebra**

by

**Peter Petersen**-

**UCLA**

This book covers the aspects of linear algebra that are included in most advanced undergraduate texts: complex vectors spaces, complex inner products, spectral theorem for normal operators, dual spaces, quotient spaces, the minimal polynomial, etc.

(

**12421**views)

**Differential Equations and Linear Algebra**

by

**Simon J.A. Malham**-

**Heriot-Watt University**

From the table of contents: Linear second order ODEs; Homogeneous linear ODEs; Non-homogeneous linear ODEs; Laplace transforms; Linear algebraic equations; Matrix Equations; Linear algebraic eigenvalue problems; Systems of differential equations.

(

**2107**views)

**n-Linear Algebra of Type I and Its Applications**

by

**W. B. V. Kandasamy, F. Smarandache**-

**InfoLearnQuest**

n-Linear Algebra of type I introduced in this book finds applications in Markov chains and Leontief economic models. Scientists and engineers can adopt this concept in fuzzy finite element analysis of mechanical structures with uncertain parameters.

(

**7036**views)

**Linear Algebra Review and Reference**

by

**Zico Kolter**-

**Stanford University**

From the tabble of contents: Basic Concepts and Notation; Matrix Multiplication; Operations and Properties; Matrix Calculus (Gradients and Hessians of Quadratic and Linear Functions, Least Squares, Eigenvalues as Optimization, etc.).

(

**13318**views)