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  2. Scaling (geometry) - Wikipedia

    en.wikipedia.org/wiki/Scaling_(geometry)

    Such a scaling changes the diameter of an object by a factor between the scale factors, the area by a factor between the smallest and the largest product of two scale factors, and the volume by the product of all three. The scaling is uniform if and only if the scaling factors are equal (v x = v y = v z). If all except one of the scale factors ...

  3. Rotation matrix - Wikipedia

    en.wikipedia.org/wiki/Rotation_matrix

    Noting that any identity matrix is a rotation matrix, and that matrix multiplication is associative, we may summarize all these properties by saying that the n × n rotation matrices form a group, which for n > 2 is non-abelian, called a special orthogonal group, and denoted by SO(n), SO(n,R), SO n, or SO n (R), the group of n × n rotation ...

  4. Transformation matrix - Wikipedia

    en.wikipedia.org/wiki/Transformation_matrix

    A reflection about a line or plane that does not go through the origin is not a linear transformation — it is an affine transformation — as a 4×4 affine transformation matrix, it can be expressed as follows (assuming the normal is a unit vector): [′ ′ ′] = [] [] where = for some point on the plane, or equivalently, + + + =.

  5. Helmert transformation - Wikipedia

    en.wikipedia.org/wiki/Helmert_transformation

    μ – scale factor, which is unitless; if it is given in ppm, it must be divided by 1,000,000 and added to 1. R – rotation matrix. Consists of three axes (small [clarification needed] rotations around each of the three coordinate axes) r x, r y, r z. The rotation matrix is an orthogonal matrix. The angles are given in either degrees or radians.

  6. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    Consequently, if all singular values of a square matrix ⁠ ⁠ are non-degenerate and non-zero, then its singular value decomposition is unique, up to multiplication of a column of ⁠ ⁠ by a unit-phase factor and simultaneous multiplication of the corresponding column of ⁠ ⁠ by the same unit-phase factor.

  7. Shear mapping - Wikipedia

    en.wikipedia.org/wiki/Shear_mapping

    If S is an n × n shear matrix, then: S has rank n and therefore is invertible; 1 is the only eigenvalue of S, so det S = 1 and tr S = n; the eigenspace of S (associated with the eigenvalue 1) has n − 1 dimensions. S is defective; S is asymmetric; S may be made into a block matrix by at most 1 column interchange and 1 row interchange operation

  8. Hausdorff dimension - Wikipedia

    en.wikipedia.org/wiki/Hausdorff_dimension

    The intuitive concept of dimension of a geometric object X is the number of independent parameters one needs to pick out a unique point inside. However, any point specified by two parameters can be instead specified by one, because the cardinality of the real plane is equal to the cardinality of the real line (this can be seen by an argument involving interweaving the digits of two numbers to ...

  9. Spinors in three dimensions - Wikipedia

    en.wikipedia.org/wiki/Spinors_in_three_dimensions

    Given a unit vector in 3 dimensions, for example (a, b, c), one takes a dot product with the Pauli spin matrices to obtain a spin matrix for spin in the direction of the unit vector. The eigenvectors of that spin matrix are the spinors for spin-1/2 oriented in the direction given by the vector. Example: u = (0.8, -0.6, 0) is a unit vector ...