Sentence examples for by setting the gradient from inspiring English sources

Equation (8) can be minimized by setting the gradient (xi left( X right)) equal to zero.

Therefore, by setting the gradient of problem (49) with respect to γ equal to zero, we can get γ ⋆ = ∑ n ∈ N v n i + ρ α n i + 1 ρN.

By setting the gradient to zero, we obtain the solution to the RMCVELM, boldsymbol{beta} = left(mathbf{H}^{T} mathbf{H} + C_{1}mathbf{I} + {C_{2}}mathbf{S}_{w} right)^{-1} mathbf{H}^{T} mathbf{T} (17).

Hence, by setting the gradient of (25) with respect to z nl equal to zero, we can get the solution z nl ⋆ which can be expressed as z nl ⋆ = x n, nl i + 1 + x tran ( l ), nl i + 1 + 1 ρ ( u n, nl i + u tran ( l ), nl i ) 2, (26).

For example, starting again by setting the gradient (7) to zero: ∂R ∂ P l n = w lk P l n 1++ 1 SINR l n − 1 − ∑ j ≠ l w j k ″ | h lj k ″ n | 2 σ 2 + ∑ i ≠ j P i n | h ij k ″ n | 2 SINR j n 1 + SINR j n = 0, (13).

The minimum value of the object function is found by setting the gradient to zero and the optimal parameter values are left overline{V},overline{W}right)={left {A}^TAright)}^{-1}{A}^Tleft u,vright) (13 where ( A ) denotes the coefficient matrix made up with the focal length f of the stereo camera, the depth Z, and the image coordinates as shown in Eq. (7).

The key idea of this paper is that by setting the above gradient to zero and by manipulating the optimality condition, one can obtain algorithms for optimizing power.

The temperature of each grid block was calculated by setting the temperature gradient along the Z direction to (18,^circ C/km and the temperature of topmost grid block to (135,^circ)C.

By setting the updating gradient term to Zero, it can be shown that this equation has a single stationary point (Wiener solution[10]) which is expressed by: W = W 0 = H U w h e r e U = R g X X R XX − 1 (13).

By setting the selection gradient values for body mass to be the same magnitude but positive, the new bivariate vectors of selection are at right angles to the original vectors, in other words more parallel to the first eigenvector of the G matrix.

This can be approximated by setting the selection gradients acting on each trait to have the same sign, in this case positive.