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The gradient of the function f(x,y) = −(cos 2 x + cos 2 y) 2 depicted as a projected vector field on the bottom plane. The gradient (or gradient vector field) of a scalar function f(x 1, x 2, x 3, …, x n) is denoted ∇f or ∇ → f where ∇ denotes the vector differential operator, del.
Slope illustrated for y = (3/2)x − 1.Click on to enlarge Slope of a line in coordinates system, from f(x) = −12x + 2 to f(x) = 12x + 2. The slope of a line in the plane containing the x and y axes is generally represented by the letter m, [5] and is defined as the change in the y coordinate divided by the corresponding change in the x coordinate, between two distinct points on the line.
The slope field can be defined for the following type of differential equations ′ = (,), which can be interpreted geometrically as giving the slope of the tangent to the graph of the differential equation's solution (integral curve) at each point (x, y) as a function of the point coordinates.
In Feynman subscript notation, = + where the notation ∇ B means the subscripted gradient operates on only the factor B. [ 1 ] [ 2 ] Less general but similar is the Hestenes overdot notation in geometric algebra . [ 3 ]
Here the final equality follows by the gradient theorem, since the function f(x) = | x | α+1 is differentiable on R n if α ≥ 1. If α < 1 then this equality will still hold in most cases, but caution must be taken if γ passes through or encloses the origin, because the integrand vector field | x | α − 1 x will fail to be defined there.
If x kilograms of salami and y kilograms of sausage costs a total of €12 then, €6×x + €3×y = €12. Solving for y gives the point-slope form = +, as above. That is, if we first choose the amount of salami x, the amount of sausage can be computed as a function = = +.
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks. [2] The adjoint state space is chosen to simplify the physical interpretation of equation ...
This suggests taking the first basis vector p 0 to be the negative of the gradient of f at x = x 0. The gradient of f equals Ax − b. Starting with an initial guess x 0, this means we take p 0 = b − Ax 0. The other vectors in the basis will be conjugate to the gradient, hence the name conjugate gradient method.