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  2. Minkowski addition - Wikipedia

    en.wikipedia.org/wiki/Minkowski_addition

    Minkowski sums act linearly on the perimeter of two-dimensional convex bodies: the perimeter of the sum equals the sum of perimeters. Additionally, if K {\textstyle K} is (the interior of) a curve of constant width , then the Minkowski sum of K {\textstyle K} and of its 180° rotation is a disk.

  3. Vector algebra relations - Wikipedia

    en.wikipedia.org/wiki/Vector_algebra_relations

    The following are important identities in vector algebra.Identities that only involve the magnitude of a vector ‖ ‖ and the dot product (scalar product) of two vectors A·B, apply to vectors in any dimension, while identities that use the cross product (vector product) A×B only apply in three dimensions, since the cross product is only defined there.

  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. Conical combination - Wikipedia

    en.wikipedia.org/wiki/Conical_combination

    If we discard the origin, we can divide all coefficients by their sum to see that a conical combination is a convex combination scaled by a positive factor. In the plane, the conical hull of a circle passing through the origin is the open half-plane defined by the tangent line to the circle at the origin plus the origin.

  6. Euclidean vector - Wikipedia

    en.wikipedia.org/wiki/Euclidean_vector

    If the dot product of two vectors is defined—a scalar-valued product of two vectors—then it is also possible to define a length; the dot product gives a convenient algebraic characterization of both angle (a function of the dot product between any two non-zero vectors) and length (the square root of the dot product of a vector by itself).

  7. Matrix addition - Wikipedia

    en.wikipedia.org/wiki/Matrix_addition

    In mathematics, matrix addition is the operation of adding two matrices by adding the corresponding entries together. For a vector , v → {\displaystyle {\vec {v}}\!} , adding two matrices would have the geometric effect of applying each matrix transformation separately onto v → {\displaystyle {\vec {v}}\!} , then adding the transformed vectors.

  8. Vector projection - Wikipedia

    en.wikipedia.org/wiki/Vector_projection

    The vector projection of a on b is a vector a 1 which is either null or parallel to b. More exactly: a 1 = 0 if θ = 90°, a 1 and b have the same direction if 0° ≤ θ < 90°, a 1 and b have opposite directions if 90° < θ ≤ 180°.

  9. 1/2 + 1/4 + 1/8 + 1/16 + ⋯ - ⋯ - Wikipedia

    en.wikipedia.org/wiki/1/2_%2B_1/4_%2B_1/8_%2B_1/...

    In mathematics, the infinite series ⁠ 1 / 2 ⁠ + ⁠ 1 / 4 ⁠ + ⁠ 1 / 8 ⁠ + ⁠ 1 / 16 ⁠ + ··· is an elementary example of a geometric series that converges absolutely. The sum of the series is 1.