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The gradient is related to the differential by the formula = for any , where is the dot product: taking the dot product of a vector with the gradient is the same as taking the directional derivative along the vector.
l = slope length α = angle of inclination. The grade (US) or gradient (UK) (also called stepth, slope, incline, mainfall, pitch or rise) of a physical feature, landform or constructed line is either the elevation angle of that surface to the horizontal or its tangent. It is a special case of the slope, where zero indicates horizontality. A ...
In Cartesian coordinates, the divergence of a continuously differentiable vector field = + + is the scalar-valued function: = = (, , ) (, , ) = + +.. As the name implies, the divergence is a (local) measure of the degree to which vectors in the field diverge.
Slope: = = In mathematics, the slope or gradient of a line is a number that describes the direction of the line on a plane. [1] Often denoted by the letter m, slope is calculated as the ratio of the vertical change to the horizontal change ("rise over run") between two distinct points on the line, giving the same number for any choice of points.
The slope of a linear equation is constant, meaning that the steepness is the same everywhere. However, many graphs such as y = x 2 {\displaystyle y=x^{2}} vary in their steepness. This means that you can no longer pick any two arbitrary points and compute the slope.
In trigonometry, the gradian – also known as the gon (from Ancient Greek γωνία (gōnía) 'angle'), grad, or grade [1] – is a unit of measurement of an angle, defined as one-hundredth of the right angle; in other words, 100 gradians is equal to 90 degrees.
In physical terms, the divergence of a vector field is the extent to which the vector field flux behaves like a source or a sink at a given point. It is a local measure of its "outgoingness" – the extent to which there are more of the field vectors exiting from an infinitesimal region of space than entering it.
When m = 1, that is when f : R n → R is a scalar-valued function, the Jacobian matrix reduces to the row vector; this row vector of all first-order partial derivatives of f is the transpose of the gradient of f, i.e. =.