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In Cartesian coordinates, for the curl is the vector field: where i, j, and k are the unit vectors for the x -, y -, and z -axes, respectively. As the name implies the curl is a measure of how much nearby vectors tend in a circular direction. In Einstein notation, the vector field has curl given by: where = ±1 or 0 is the Levi-Civita parity ...
Gradient. The gradient, represented by the blue arrows, denotes the direction of greatest change of a scalar function. The values of the function are represented in greyscale and increase in value from white (low) to dark (high). In vector calculus, the gradient of a scalar-valued differentiable function of several variables is the vector field ...
In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme . In computer graphics, the term swatch [1] has come to mean a palette of active ...
Vector operator. A vector operator is a differential operator used in vector calculus. Vector operators include the gradient, divergence, and curl : Gradient is a vector operator that operates on a scalar field, producing a vector field. Divergence is a vector operator that operates on a vector field, producing a scalar field.
Gradient method. In optimization, a gradient method is an algorithm to solve problems of the form. with the search directions defined by the gradient of the function at the current point. Examples of gradient methods are the gradient descent and the conjugate gradient .
The gradient theorem implies that line integrals through gradient fields are path-independent. In physics this theorem is one of the ways of defining a conservative force. By placing φ as potential, ∇φ is a conservative field. Work done by conservative forces does not depend on the path followed by the object, but only the end points, as ...
Gradient network. In network science, a gradient network is a directed subnetwork of an undirected "substrate" network where each node has an associated scalar potential and one out-link that points to the node with the smallest (or largest) potential in its neighborhood, defined as the union of itself and its neighbors on the substrate network.
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