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  2. Multidimensional discrete convolution - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_discrete...

    Similar to the one-dimensional case, an asterisk is used to represent the convolution operation. The number of dimensions in the given operation is reflected in the number of asterisks. For example, an M-dimensional convolution would be written with M asterisks. The following represents a M-dimensional convolution of discrete signals:

  3. Dyadics - Wikipedia

    en.wikipedia.org/wiki/Dyadics

    The dot product of a dyadic with a vector gives another vector, and taking the dot product of this result gives a scalar derived from the dyadic. The effect that a given dyadic has on other vectors can provide indirect physical or geometric interpretations. Dyadic notation was first established by Josiah Willard Gibbs in 1884. The notation and ...

  4. Two-vector - Wikipedia

    en.wikipedia.org/wiki/Two-vector

    A two-vector or bivector [1] is a tensor of type () and it is the dual of a two-form, meaning that it is a linear functional which maps two-forms to the real numbers (or more generally, to scalars). The tensor product of a pair of vectors is a two-vector. Then, any two-form can be expressed as a linear combination of tensor products of pairs of ...

  5. Tensor product - Wikipedia

    en.wikipedia.org/wiki/Tensor_product

    The tensor product of two vector spaces is a vector space that is defined up to an isomorphism.There are several equivalent ways to define it. Most consist of defining explicitly a vector space that is called a tensor product, and, generally, the equivalence proof results almost immediately from the basic properties of the vector spaces that are so defined.

  6. Recurrence relation - Wikipedia

    en.wikipedia.org/wiki/Recurrence_relation

    A famous example is the recurrence for the Fibonacci numbers, = + where the order is two and the linear function merely adds the two previous terms. This example is a linear recurrence with constant coefficients , because the coefficients of the linear function (1 and 1) are constants that do not depend on n . {\displaystyle n.}

  7. Raising and lowering indices - Wikipedia

    en.wikipedia.org/wiki/Raising_and_lowering_indices

    Raising and lowering is then done in coordinates. Given a vector with components , we can contract with the metric to obtain a covector: = and this is what we mean by lowering the index. Conversely, contracting a covector with the inverse metric gives a vector:

  8. Comparison of linear algebra libraries - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_linear...

    C, C++, Fortran 2003 2023.1 / 03.2023 Non-free Intel Simplified Software License Numerical analysis library optimized for Intel CPUs and GPUs. C++ SYCL based reference API implementation available in source for free. Math.NET Numerics: C. Rüegg, M. Cuda, et al. C# 2009 5.0.0 / 04.2022 Free MIT License

  9. Screw theory - Wikipedia

    en.wikipedia.org/wiki/Screw_theory

    Since ε 2 = 0 for dual numbers, exp(aε) = 1 + aε, all other terms of the exponential series vanishing. Let F = {1 + εr : r ∈ H}, ε 2 = 0. Note that F is stable under the rotation q → p −1 qp and under the translation (1 + εr)(1 + εs) = 1 + ε(r + s) for any vector quaternions r and s. F is a 3-flat in the eight-dimensional space of ...