(Introduction to linear transformations.) |
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The clearest '''example''' of a linear transformation is '''matrix multiplication'''—L(x)=Ax. Multiplying by a matrix affects a given vector in several ways—rotation, dilation—which includes expansion and contraction, and reflection or inversion. Some matrices have a known and cataloged effect on the vectors they multiply. Most matrices will give a combination of these effects. | The clearest '''example''' of a linear transformation is '''matrix multiplication'''—L(x)=Ax. Multiplying by a matrix affects a given vector in several ways—rotation, dilation—which includes expansion and contraction, and reflection or inversion. Some matrices have a known and cataloged effect on the vectors they multiply. Most matrices will give a combination of these effects. | ||
| − | Matrix multiplication produces linear transformations from one dimension to another, or within the same dimension. For example, multiplication by a square matrix of dimension n returns a transformation within dimension n. This is because only a vector of the same dimension can be multiplied by such a matrix. An m x n matrix will induce a transformation from R<sup>n</sup> to R<sup>m</sup>. | + | Matrix multiplication produces linear transformations from one dimension to another, or within the same dimension. For example, multiplication by a square matrix of dimension n returns a transformation within dimension n. This is because only a vector of the same dimension can be multiplied by such a matrix. An m x n matrix will induce a transformation from R<sup>n</sup> to R<sup>m</sup>. |
'''Dilations''' | '''Dilations''' | ||
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The most well known transformation is given by the multiplication of a vector with the identity matrix. This returns the original vector unchanged. If I<sub>n</sub> is scaled by α, a vector multiplied by α I<sub>n</sub> will be scaled by α. For α >1, this transformation is an expansion, or enlargement. For α<1, the transformation is a contraction. | The most well known transformation is given by the multiplication of a vector with the identity matrix. This returns the original vector unchanged. If I<sub>n</sub> is scaled by α, a vector multiplied by α I<sub>n</sub> will be scaled by α. For α >1, this transformation is an expansion, or enlargement. For α<1, the transformation is a contraction. | ||
| − | '''Rotations''' | + | '''Rotations''' If multiplied by a matrix [cosθ -sinθ, sinθ cosθ] a vector will be rotated counterclockwise by angle θ. As a case in point, the identity matrix represents a rotation by angle 0. |
| − | If multiplied by a matrix [cosθ -sinθ, sinθ cosθ] a vector will be rotated counterclockwise by angle θ. As a case in point, the identity matrix represents a rotation by angle 0. | + | |
| − | '''Reflection or Inversion''' | + | '''Reflection or Inversion''' |
Multiplication by the matrix [1 0, 0 -1] gives reflection over the x axis in R<sup>2</sup>. It is possible to multiply by matrices to reflect over axes and lines. | Multiplication by the matrix [1 0, 0 -1] gives reflection over the x axis in R<sup>2</sup>. It is possible to multiply by matrices to reflect over axes and lines. | ||
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If a linear transformation preserves the distances between points in the figure being transformed, it is called an isometry. | If a linear transformation preserves the distances between points in the figure being transformed, it is called an isometry. | ||
| − | '''How to determine the matrix that causes a linear transformation:''' | + | '''How to determine the matrix that causes a linear transformation:''' |
We know that the vectors of the matrix A will be the same as the transformations it gives to the vectors that make up the standard basis for the space from which it is transformed. | We know that the vectors of the matrix A will be the same as the transformations it gives to the vectors that make up the standard basis for the space from which it is transformed. | ||
| − | ''A special case of linear transformations is given by '''eigenvectors '''and '''eigenvalues'''. In a linear transformation over a constant vector space, there will be certain vectors that when multiplied by the matrix A, give a multiple of themselves. The multiple is denoted by the eigenvalue. These special vectors are the eigenvectors of the matrix.'' | + | ''A special case of linear transformations is given by '''eigenvectors '''and '''eigenvalues'''. In a linear transformation over a constant vector space, there will be certain vectors that when multiplied by the matrix A, give a multiple of themselves. The multiple is denoted by the eigenvalue. These special vectors are the eigenvectors of the matrix.'' |
'''Properties:''' | '''Properties:''' | ||
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A line will always be transformed to another line. | A line will always be transformed to another line. | ||
| − | A transformation of 0 will give 0. | + | A transformation of 0 will give 0. |
'''Isomorphisms:''' The word derives from the Greek roots iso-, which means “equal” or “same” and morphos which means form, shape or structure. Isomorphic can refer to vector spaces—isomorphic vector spaces contain the same algebraic properties. | '''Isomorphisms:''' The word derives from the Greek roots iso-, which means “equal” or “same” and morphos which means form, shape or structure. Isomorphic can refer to vector spaces—isomorphic vector spaces contain the same algebraic properties. | ||
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'''Applications:''' One important application of linear transformations is cryptology. | '''Applications:''' One important application of linear transformations is cryptology. | ||
| − | <br>'''References:''' | + | <br>'''References:''' |
| − | Breitenbach, Jerome R. "A Mathematics Companion for Science and Engineering Students." New York: Oxford University Press, 2008. | + | Breitenbach, Jerome R. "A Mathematics Companion for Science and Engineering Students." New York: Oxford University Press, 2008. |
| − | Kolman, Bernard and Hill, David. "Elementary Linear Algebra with Applications and Labs: Custom Edition for Purdue University." Boston: Pearson Learning Solutions, 2004. | + | Kolman, Bernard and Hill, David. "Elementary Linear Algebra with Applications and Labs: Custom Edition for Purdue University." Boston: Pearson Learning Solutions, 2004. |
Rowland, Todd and Weisstein, Eric W. "Linear Transformation." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LinearTransformation.html <br> | Rowland, Todd and Weisstein, Eric W. "Linear Transformation." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LinearTransformation.html <br> | ||
| − | + | <span id="fck_dom_range_temp_1292433960937_415" /> | |
| + | |||
| + | [[Category:MA265Fall2010Walther]] | ||
Latest revision as of 08:26, 15 December 2010
Linear Transformations
A linear transformation is a mapping from one vector space to another. It must fulfill the following conditions:
Commutativity: F(x + y) = F(x) + F(y); and
Distributivity: F(λx)= λF(x), where λ is a scalar
The clearest example of a linear transformation is matrix multiplication—L(x)=Ax. Multiplying by a matrix affects a given vector in several ways—rotation, dilation—which includes expansion and contraction, and reflection or inversion. Some matrices have a known and cataloged effect on the vectors they multiply. Most matrices will give a combination of these effects.
Matrix multiplication produces linear transformations from one dimension to another, or within the same dimension. For example, multiplication by a square matrix of dimension n returns a transformation within dimension n. This is because only a vector of the same dimension can be multiplied by such a matrix. An m x n matrix will induce a transformation from Rn to Rm.
Dilations
The most well known transformation is given by the multiplication of a vector with the identity matrix. This returns the original vector unchanged. If In is scaled by α, a vector multiplied by α In will be scaled by α. For α >1, this transformation is an expansion, or enlargement. For α<1, the transformation is a contraction.
Rotations If multiplied by a matrix [cosθ -sinθ, sinθ cosθ] a vector will be rotated counterclockwise by angle θ. As a case in point, the identity matrix represents a rotation by angle 0.
Reflection or Inversion
Multiplication by the matrix [1 0, 0 -1] gives reflection over the x axis in R2. It is possible to multiply by matrices to reflect over axes and lines.
Isometry
If a linear transformation preserves the distances between points in the figure being transformed, it is called an isometry.
How to determine the matrix that causes a linear transformation:
We know that the vectors of the matrix A will be the same as the transformations it gives to the vectors that make up the standard basis for the space from which it is transformed.
A special case of linear transformations is given by eigenvectors and eigenvalues. In a linear transformation over a constant vector space, there will be certain vectors that when multiplied by the matrix A, give a multiple of themselves. The multiple is denoted by the eigenvalue. These special vectors are the eigenvectors of the matrix.
Properties:
Taking derivatives and taking the inner product are functions that give linear transformations.
A line will always be transformed to another line.
A transformation of 0 will give 0.
Isomorphisms: The word derives from the Greek roots iso-, which means “equal” or “same” and morphos which means form, shape or structure. Isomorphic can refer to vector spaces—isomorphic vector spaces contain the same algebraic properties.
Applications: One important application of linear transformations is cryptology.
References:
Breitenbach, Jerome R. "A Mathematics Companion for Science and Engineering Students." New York: Oxford University Press, 2008.
Kolman, Bernard and Hill, David. "Elementary Linear Algebra with Applications and Labs: Custom Edition for Purdue University." Boston: Pearson Learning Solutions, 2004.
Rowland, Todd and Weisstein, Eric W. "Linear Transformation." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/LinearTransformation.html
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