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  2. 25 Great Resume Templates For All Jobs - AOL

    www.aol.com/news/2014-08-27-great-resume...

    These 25 templates include appropriate examples for positions in finance, admin, graphic design, academia, and more. Some of the designs we selected are traditional and some are more creative, but ...

  3. Multivariate random variable - Wikipedia

    en.wikipedia.org/wiki/Multivariate_random_variable

    Here the random vector is the vector of random returns on the individual assets, and the portfolio return p (a random scalar) is the inner product of the vector of random returns with a vector w of portfolio weights — the fractions of the portfolio placed in the respective assets.

  4. Scalar (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Scalar_(mathematics)

    A scalar is an element of a field which is used to define a vector space.In linear algebra, real numbers or generally elements of a field are called scalars and relate to vectors in an associated vector space through the operation of scalar multiplication (defined in the vector space), in which a vector can be multiplied by a scalar in the defined way to produce another vector.

  5. Scale invariance - Wikipedia

    en.wikipedia.org/wiki/Scale_invariance

    Another example of a scale-invariant classical field theory is the massless scalar field (note that the name scalar is unrelated to scale invariance). The scalar field, φ(x, t) is a function of a set of spatial variables, x, and a time variable, t. Consider first the linear theory.

  6. Scalar multiplication - Wikipedia

    en.wikipedia.org/wiki/Scalar_multiplication

    The space of vectors may be considered a coordinate space where elements are associated with a list of elements from K. The units of the field form a group K × and the scalar-vector multiplication is a group action on the coordinate space by K ×. The zero of the field acts on the coordinate space to collapse it to the zero vector.

  7. Eigendecomposition of a matrix - Wikipedia

    en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix

    If A is Hermitian and full-rank, the basis of eigenvectors may be chosen to be mutually orthogonal. The eigenvalues are real. The eigenvectors of A −1 are the same as the eigenvectors of A. Eigenvectors are only defined up to a multiplicative constant. That is, if Av = λv then cv is also an eigenvector for any scalar c ≠ 0.

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