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  2. R-value (insulation) - Wikipedia

    en.wikipedia.org/wiki/R-value_(insulation)

    The R-value is the building industry term [3] for thermal resistance "per unit area." [4] It is sometimes denoted RSI-value if the SI units are used. [5] An R-value can be given for a material (e.g., for polyethylene foam), or for an assembly of materials (e.g., a wall or a window). In the case of materials, it is often expressed in terms of R ...

  3. Identity matrix - Wikipedia

    en.wikipedia.org/wiki/Identity_matrix

    The th column of an identity matrix is the unit vector, a vector whose th entry is 1 and 0 elsewhere. The determinant of the identity matrix is 1, and its trace is . The identity matrix is the only idempotent matrix with non-zero determinant. That is, it is the only matrix such that:

  4. List of insulation materials - Wikipedia

    en.wikipedia.org/wiki/List_of_insulation_materials

    This is a list of insulation materials used around the world. Typical R-values are given for various materials and structures as approximations based on the average of available figures and are sorted by lowest value. R-value at 1 m gives R-values normalised to a 1 metre (3 ft 3 in) thickness and sorts by median value of the range.

  5. Lankford coefficient - Wikipedia

    en.wikipedia.org/wiki/Lankford_coefficient

    The Lankford coefficient (also called Lankford value, R-value, or plastic strain ratio) [1] is a measure of the plastic anisotropy of a rolled sheet metal. This scalar quantity is used extensively as an indicator of the formability of recrystallized low-carbon steel sheets.

  6. Woodbury matrix identity - Wikipedia

    en.wikipedia.org/wiki/Woodbury_matrix_identity

    In mathematics, specifically linear algebra, the Woodbury matrix identity – named after Max A. Woodbury [1] [2] – says that the inverse of a rank-k correction of some matrix can be computed by doing a rank-k correction to the inverse of the original matrix.

  7. Rank factorization - Wikipedia

    en.wikipedia.org/wiki/Rank_factorization

    Every finite-dimensional matrix has a rank decomposition: Let be an matrix whose column rank is . Therefore, there are r {\textstyle r} linearly independent columns in A {\textstyle A} ; equivalently, the dimension of the column space of A {\textstyle A} is r {\textstyle r} .