Sentence examples for in gradient of objective from inspiring English sources

For example, the terms in gradient of objective function F with respect to nonnegative parameter A are divided into positive terms and negative terms ∂ F ∂A = ∂ F ∂A + − ∂ F ∂A − where ∂ F ∂A + > 0 and ∂ F ∂A − > 0. The multiplicative update rule is yielded by A ← A ⊗ ∂ F ∂A − ⊘ ∂ F ∂A + (4).

The idea of this method is to change distributions P in the direction of the gradient of objective function W (P) in order to reach a local maximum of W (P). A standard gradient ascent algorithm in the present context can be characterized by (10).

On the other hand, in gradient-based methods, the gradient of objective function subject to optimization variables is needed.

Generally, the algorithm consists of choosing at the k th iteration a point X k + 1) in a direction lying in the half space defined by the gradient of objective function Υ, defined by matrix D k), which verifies [22]: vec { ∇ Υ ( X ( k ) ) } T vec { D ( k ) } < 0. (17).

At each conjugate-gradient iteration, it is only involved with computing the gradient of objective function.

The domain decomposition of the wavefield solutions is used to compute in parallel the gradient of the objective function and the diagonal Hessian, this latter providing a suitable scaling of the gradient.

In many applications, the gradient of the objective function is approximated using finite differences.

Although the function to be maximized is not concave [because of the term J PP )], we simply start from an initial solution and iteratively choose a new permutation matrix in the direction of the gradient of the objective function.

Furthermore, instead of adopting the commonly used finite difference approximation, we make use of sensitivity equations to evaluate the gradient of the objective function in the optimization procedure, previously mentioned in (28).

In order to converge rapidly, the gradient of the objective function must be computed at each iteration of the algorithm.

The gradient of the objective functional, in the case that the surface to be optimized is given in a finite-parametric form, is derived from the shape gradient.