Linear algebra
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Linear algebra is the branch of mathematics concerned with the study of vectors, vector spaces (also called linear spaces), linear maps (also called linear transformations), and systems of linear equations. Vector spaces are a central theme in modern mathematics; thus, linear algebra is widely used in both abstract algebra and functional analysis. Linear algebra also has a concrete representation in analytic geometry and it is generalized in operator theory. It has extensive applications in the natural sciences and the social sciences, since nonlinear models can often be approximated by linear ones.
One of the applications of linear algebra is the solution of simultaneous linear equations. The simplest case is when the number of unknowns is equal to the number of equations. Therefore, one could begin with the problem of solving n simultaneous linear equations in n unknowns.^{[1]}
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[edit] History
The history of modern linear algebra dates back to the early 1840s. In 1843, William Rowan Hamilton introduced quaternions, which describe mechanics in threedimensional space. In 1844, Hermann Grassmann published his book Die lineale Ausdehnungslehre (see References). Arthur Cayley introduced matrices, one of the most fundamental linear algebraic ideas, in 1857. Despite these early developments, linear algebra has been developed primarily in the twentieth century. It was the focus of one of the first international mathematical societies, the Quaternion Society, which aimed to study allied systems of mathematics.
Matrices were poorlydefined before the development of ring theory within abstract algebra. With the coming of special relativity, many practitioners gained appreciation of the subtleties of linear algebra. For instance, in 1914 Ludwik Silberstein included an introduction to matrices in his Theory of Relativity (pp.602). Meanwhile, in pure mathematics the routine application of Cramer's rule to solve partial differential equations led to the inclusion of linear algebra in standard coursework at universities. Edward Thomas Copson wrote, for instance,
When I went to Edinburgh as a young lecturer in 1922, I was surprised to find how different the curriculum was from that at Oxford. It included topics such as Lebesgue integration, matrix theory, numerical analysis, Riemannian geometry, of which I knew nothing...^{[2]}
Francis Galton initiated the use of correlation coefficients in 1888. Often more than one random variable is in play and may be crosscorrelated. In statistical analysis of multivariate random variables the correlation matrix is a natural tool. Thus, statistical study of such random vectors helped establish matrix usage.
More recent developments followed the formulation of the vector space concept into an algebraic structure, and the growth of functional analysis. One can see a diverse set of applications in the list of matrices.
[edit] Elementary introduction
Linear algebra had its beginnings in the study of vectors in Cartesian 2space and 3space. A vector, here, is a directed line segment, characterized by both its magnitude, represented by length, and its direction. Vectors can be used to represent physical entities such as forces, and they can be added to each other and multiplied with scalars, thus forming the first example of a real vector space.
Modern linear algebra has been extended to consider spaces of arbitrary or infinite dimension. A vector space of dimension n is called an nspace. Most of the useful results from 2 and 3space can be extended to these higher dimensional spaces. Although people cannot easily visualize vectors in nspace, such vectors or ntuples are useful in representing data. Since vectors, as ntuples, are ordered lists of n components, it is possible to summarize and manipulate data efficiently in this framework. For example, in economics, one can create and use, say, 8dimensional vectors or 8tuples to represent the gross national product of 8 countries. One can decide to display the GNP of 8 countries for a particular year, where the countries' order is specified, for example, (United States, United Kingdom, Armenia, Germany, Brazil, India, Japan, Bangladesh), by using a vector (v_{1}, v_{2}, v_{3}, v_{4}, v_{5}, v_{6}, v_{7}, v_{8}) where each country's GNP is in its respective position.
A vector space (or linear space), as a purely abstract concept about which theorems are proved, is part of abstract algebra, and is well integrated into this discipline. Some striking examples of this are the group of invertible linear maps or matrices, and the ring of linear maps of a vector space. Linear algebra also plays an important part in analysis, notably, in the description of higher order derivatives in vector analysis and the study of tensor products and alternating maps.
In this abstract setting, the scalars with which an element of a vector space can be multiplied need not be numbers. The only requirement is that the scalars form a mathematical structure, called a field. In applications, this field is usually the field of real numbers or the field of complex numbers. Linear maps take elements from a linear space to another (or to itself), in a manner that is compatible with the addition and scalar multiplication given on the vector space(s). The set of all such transformations is itself a vector space. If a basis for a vector space is fixed, every linear transform can be represented by a table of numbers called a matrix. The detailed study of the properties of and algorithms acting on matrices, including determinants and eigenvectors, is considered to be part of linear algebra.
One can say quite simply that the linear problems of mathematics  those that exhibit linearity in their behavior  are those most likely to be solved. For example differential calculus does a great deal with linear approximation to functions. The difference from nonlinear problems is very important in practice.
The general method of finding a linear way to look at a problem, expressing this in terms of linear algebra, and solving it, if need be by matrix calculations, is one of the most generally applicable in mathematics.
[edit] Some useful theorems
 Every vector space has a basis.^{[3]}
 Any two bases of the same vector space have the same cardinality; equivalently, the dimension of a vector space is welldefined.
 A matrix is invertible if and only if its determinant is nonzero.
 A matrix is invertible if and only if the linear map represented by the matrix is an isomorphism.
 If a square matrix has a left inverse or a right inverse then it is invertible (see invertible matrix for other equivalent statements).
 A matrix is positive semidefinite if and only if each of its eigenvalues is greater than or equal to zero.
 A matrix is positive definite if and only if each of its eigenvalues is greater than zero.
 The spectral theorem (regarding diagonalizable matrices).
[edit] Generalizations and related topics
Since linear algebra is a successful theory, its methods have been developed in other parts of mathematics. In module theory one replaces the field of scalars by a ring. In multilinear algebra one considers multivariable linear transformations, that is, mappings which are linear in each of a number of different variables. This line of inquiry naturally leads to the idea of the tensor product. In the spectral theory of operators control of infinitedimensional matrices is gained, by applying mathematical analysis in a theory that is not purely algebraic. In all these cases the technical difficulties are much greater.
[edit] See also
[edit] Note
 ^ Strang, G. 1980. Linear algebra and its Aplications. Second edition. New York: Academic Press. ISBN 012673660X.
 ^ E.T. Copson, Preface to Partial Differential Equations, 1973
 ^ The existence of a basis is straightforward for finitely generated vector spaces, but in full generality it is logically equivalent to the axiom of choice.
[edit] References
[edit] Textbooks
 Lay, David C. (August 22, 2005), Linear Algebra and Its Applications (3rd ed.), Addison Wesley, ISBN 9780321287137
 Meyer, Carl D. (February 15, 2001), Matrix Analysis and Applied Linear Algebra, Society for Industrial and Applied Mathematics (SIAM), ISBN 9780898714548. Available online at http://www.matrixanalysis.com/DownloadChapters.html
 Anton, Howard (2005), Elementary Linear Algebra (Applications Version) (9th ed.), Wiley International
 Leon, Steven J. (2006), Linear Algebra With Applications (7th ed.), Pearson Prentice Hall
[edit] Free Online books
 Beezer, Rob, A First Course in Linear Algebra
 Connell, Edwin H., Elements of Abstract and Linear Algebra
 Hefferon, Jim, Linear Algebra excellent textbook with complete solutions manual
[edit] History
 FearnleySander, Desmond, "Hermann Grassmann and the Creation of Linear Algebra" (via JSTOR), American Mathematical Monthly 86 (1979), pp. 809–817.
 Grassmann, Hermann, Die lineale Ausdehnungslehre ein neuer Zweig der Mathematik: dargestellt und durch Anwendungen auf die übrigen Zweige der Mathematik, wie auch auf die Statik, Mechanik, die Lehre vom Magnetismus und die Krystallonomie erläutert, O. Wigand, Leipzig, 1844.
[edit] External links
Wikibooks has more on the topic of 
 International Linear Algebra Society
 MIT Professor Gilbert Strang's Linear Algebra Course Homepage : MIT Course Website
 MIT Linear Algebra Lectures: free videos from MIT OpenCourseWare
 Streaming MIT Linear Algebra Lectures at Google Video
 Linear Algebra Toolkit.
 Linear Algebra on MathWorld.
 Linear Algebra overview and notation summary on PlanetMath.
 Matrix and Linear Algebra Terms on Earliest Known Uses of Some of the Words of Mathematics
 Earliest Uses of Symbols for Matrices and Vectors on Earliest Uses of Various Mathematical Symbols
 Linear Algebra by Elmer G. Wiens. Interactive web pages for vectors, matrices, linear equations, etc.
 Linear Algebra Solved Problems: Interactive forums for discussion of linear algebra problems, from the lowest up to the hardest level (Putnam).
 Linear Algebra for Informatics. José FigueroaO'Farrill, University of Edinburgh
 Online Notes / Linear Algebra Paul Dawkins, Lamar University
 Elementary Linear Algebra textbook with solutions
 Linear Algebra Wiki
 Linear algebra (math 21b) homework and exercises

