Exact(1)
Therefore, it is clearly shown from (3) that minimizing the mutual information is roughly equivalent to finding the direction in which the negentropy is maximized.
Similar(59)
The main reasons for choosing the eigenvectors of the Hessian as search directions are: Computing the eigenvectors of the Hessian is related to finding the directions with extreme values of the second derivatives, i.e., directions of extreme normal-to-isosurface change.
In the implementation of the algorithm, generally we need to be devoted to finding the positive direction of the tangent vector at a point on Γ w ( 0 ) which keeps the sign of the determinant | D H w ( 0 ) ( w, λ ) p T | invariant.
Maximizing variance as finding the "direction of maximum stretch" of the covariance matrix.
To better understand problem (19), we provide its geometric explanation in Fig. 2. Since v 2 is a unit-norm vector in (mathbb {C}^{M_{2}}), finding the solution of problem (19) is equivalent to finding a direction vector that maximizes the minimum of projections of h 12 and (sqrt {lambda _{text {max}}}{mathbf {h}}_{22}) on it.
Eventually they will end up finding the right direction.
The direction-finding algorithm is applied to find, indicating the direction of steepest ascent from.
To get directions, start by finding the place you want to go.
Direction refers to finding a way.
In order to guarantee the direction finding precision, a two-dimensional interpolation is introduced.
This important property allows us to construct the direction finding subproblems only considering the constraints in the working set (I_{k}).
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