- Key People:
- James H. Wilkinson
- Related Topics:
- algebra
- matrix
- linear equation
- linear transformation
linear algebra
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What is linear algebra?
Linear algebra is a branch of mathematics that deals with vectors and matrices, and more generally with vector spaces and linear transformations. Vector spaces are sets of multidimensional quantities, known as vectors, together with a set of one-dimensional quantities, known as scalars. Linear transformations are functions that send, or "map," one vector to another vector. Linear algebra is well understood, and its value lies in its many applications, from mathematical physics to modern algebra and coding theory.
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What is a linear equation?
In mathematics, a linear equation is a statement where a first-degree polynomial equals a constant. In n variables, it takes the form (a_0 + a_1x_1 + … + a_nx_n = c), where (x_1, …, x_n) are variables, (a_0, …, a_n) are constant coefficients, and c is a constant.
A linear equation with two variables represents a straight line on a Cartesian coordinate system. The simplest of these is (y = x), which, when graphed, produces a straight line that traverses the lower-left and upper-right quadrants at a 45-degree angle, passing through the origin.
Linear algebra deals with mathematical structures closed under addition and scalar multiplication. It includes the theory of systems of linear equations, matrices, determinants, vector spaces, and linear transformations.
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What is a vector in linear algebra?
In linear algebra, a vector is a quantity that has both magnitude and direction and can be readily applied to physical applications. Vectors are often expressed using coordinates. In two dimensions, a vector can be defined by a pair of coordinates ((a_1, a_2)) describing an arrow from the origin ((0, 0)) to the point ((a_1, a_2)). In three dimensions, a vector is expressed using three coordinates ((a_1, a_2, a_3)), and this idea extends to any number of dimensions.
A vector space is a set of multidimensional quantities, known as vectors, together with a set of one-dimensional quantities, known as scalars, such that vectors can be added together and vectors can be multiplied by scalars while preserving the ordinary arithmetic properties.
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What is a matrix and how is it used in linear algebra?
In linear algebra, a matrix is a rectangular array of numbers arranged in rows and columns. Matrices serve as a compact way to perform calculations and are used to represent linear transformations, which are functions mapping one vector to another while preserving vector addition and scalar multiplication.
When doing computations, linear transformations are treated as matrices. Matrices can be added, subtracted, or multiplied. Matrix multiplication is essential for various applications, including plotting graphs, rendering images, analyzing electrical circuits, guiding robots, developing medical scans, encrypting messages, facilitating electronic communication, and performing statistical analysis. Matrices also appear in systems of simultaneous equations.
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How do you add, subtract, and multiply matrices?
Matrices are fundamental in various fields, serving as rectangular arrays of numbers for compact calculations.
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Addition: If matrices ( A ) and ( B ) are the same size (( m \times n )), their sum ( S = A + B ) is an ( m \times n ) matrix where each element ( s_{ij} = a_{ij} + b_{ij} ).
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Scalar Multiplication: A matrix ( A ) multiplied by a scalar ( c ) results in a matrix ( cA ) where each element is ( ca_{ij} ).
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Matrix Multiplication: The product of matrix ( A ) and matrix ( B ) is defined only if the number of columns in ( A ) equals the number of rows in ( B ). If ( A ) is an ( m \times n ) matrix and ( B ) is an ( n \times p ) matrix, then the product ( C ) is an ( m \times p ) matrix. The element ( c_{ij} ) is found by multiplying corresponding elements in the ( i )th row of ( A ) and the ( j )th column of ( B ) and summing the results. Unlike ordinary numbers, matrix multiplication isn't commutative; that is, ( AB ) doesn't always equal ( BA ). However, it is associative and distributive.
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How is linear algebra used to solve real-world problems?
Linear algebra is a branch of mathematics that deals with vectors, matrices, vector spaces, and linear transformations. It has many applications in diverse fields, including mathematical physics, modern algebra, and coding theory.
Many applied mathematics problems involve solving systems of linear equations, which can occur naturally or as part of the solution process. These systems are expressed using matrix-vector notation, such as (Ax = b), where (A) is the coefficient matrix, (x) is the vector of unknown variables, and (b) is a given column vector. Solving linear systems with up to 1,000 variables is now relatively straightforward.
Vectors, understood as quantities with magnitude and direction, are central to linear algebra and have ready physical applications. They are often expressed using coordinates, such as ((a_1, a_2)) in two dimensions, representing an arrow from the origin to the point ((a_1, a_2)).
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linear algebra, mathematical discipline that deals with vectors and matrices and, more generally, with vector spaces and linear transformations. Unlike other parts of mathematics that are frequently invigorated by new ideas and unsolved problems, linear algebra is very well understood. Its value lies in its many applications, from mathematical physics to modern algebra and coding theory.
Vectors and vector spaces
Linear algebra usually starts with the study of vectors, which are understood as quantities having both magnitude and direction. Vectors lend themselves readily to physical applications. For example, consider a solid object that is free to move in any direction. When two forces act at the same time on this object, they produce a combined effect that is the same as a single force. To picture this, represent the two forces v and w as arrows; the direction of each arrow gives the direction of the force, and its length gives the magnitude of the force. The single force that results from combining v and w is called their sum, written v + w. In the , v + w corresponds to the diagonal of the parallelogram formed from adjacent sides represented by v and w.
Vectors are often expressed using coordinates. For example, in two dimensions a vector can be defined by a pair of coordinates (a1, a2) describing an arrow going from the origin (0, 0) to the point (a1, a2). If one vector is (a1, a2) and another is (b1, b2), then their sum is (a1 + b1, a2 + b2); this gives the same result as the parallelogram (see the ). In three dimensions a vector is expressed using three coordinates (a1, a2, a3), and this idea extends to any number of dimensions.
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Representing vectors as arrows in two or three dimensions is a starting point, but linear algebra has been applied in contexts where this is no longer appropriate. For example, in some types of differential equations the sum of two solutions gives a third solution, and any constant multiple of a solution is also a solution. In such cases the solutions can be treated as vectors, and the set of solutions is a vector space in the following sense. In a vector space any two vectors can be added together to give another vector, and vectors can be multiplied by numbers to give “shorter” or “longer” vectors. The numbers are called scalars because in early examples they were ordinary numbers that altered the scale, or length, of a vector. For example, if v is a vector and 2 is a scalar, then 2v is a vector in the same direction as v but twice as long. In many modern applications of linear algebra, scalars are no longer ordinary real numbers, but the important thing is that they can be combined among themselves by addition, subtraction, multiplication, and division. For example, the scalars may be complex numbers, or they may be elements of a finite field such as the field having only the two elements 0 and 1, where 1 + 1 = 0. The coordinates of a vector are scalars, and when these scalars are from the field of two elements, each coordinate is 0 or 1, so each vector can be viewed as a particular sequence of 0s and 1s. This is very useful in digital processing, where such sequences are used to encode and transmit data.
Linear transformations and matrices
Vector spaces are one of the two main ingredients of linear algebra, the other being linear transformations (or “operators” in the parlance of physicists). Linear transformations are functions that send, or “map,” one vector to another vector. The simplest example of a linear transformation sends each vector to c times itself, where c is some constant. Thus, every vector remains in the same direction, but all lengths are multiplied by c. Another example is a rotation, which leaves all lengths the same but alters the directions of the vectors. Linear refers to the fact that the transformation preserves vector addition and scalar multiplication. This means that if T is a linear transformation sending a vector v to T(v), then for any vectors v and w, and any scalar c, the transformation must satisfy the properties T(v + w) = T(v) + T(w) and T(cv) = cT(v).
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When doing computations, linear transformations are treated as matrices. A matrix is a rectangular arrangement of scalars, and two matrices can be added or multiplied as shown in the . The product of two matrices shows the result of doing one transformation followed by another (from right to left), and if the transformations are done in reverse order the result is usually different. Thus, the product of two matrices depends on the order of multiplication; if S and T are square matrices (matrices with the same number of rows as columns) of the same size, then ST and TS are rarely equal. The matrix for a given transformation is found using coordinates. For example, in two dimensions a linear transformation T can be completely determined simply by knowing its effect on any two vectors v and w that have different directions. Their transformations T(v) and T(w) are given by two coordinates; therefore, only four coordinates, two for T(v) and two for T(w), are needed to specify T. These four coordinates are arranged in a 2-by-2 matrix. In three dimensions three vectors u, v, and w are needed, and to specify T(u), T(v), and T(w) one needs three coordinates for each. This results in a 3-by-3 matrix.