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Moreover, we present an approximate variable preconditioner which does not compute the matrix inverse.
Then we can also get the approximate variable preconditioners with respect to ( A T A ) − 1 / 2 on the ( k + 1 ) th iteration: D j j k + 1 = Re [ ( D ¯ j j k + 1 ) 1 / 2 ]. (30).
Therefore, we can obtain the approximate variable preconditioners with respect to ( A T A ) − 1 on the ( k + 1 ) th iteration: D ¯ j j k + 1 = { x j k / ( A T P Q k A x k ) j, if x j k ≠ 0 and ( A T P Q k A x k ) j ≠ 0 ; D ¯ j j k otherwise.
We used a t-statistic to approximate variable significance, specifying an upper bound for the degrees of freedom as the number of observations less the number of fixed-effect parameters.
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For both HDG methods, the approximate vector field converges with the optimal order of k + 1 in the L2-norm, when polynomials of degree k are used to represent all the approximate variables.
In addition, all the approximate variables (including the approximate velocity and gradient) converge with the optimal order of k + 1 in the L2-norm, when polynomials of degree k ⩾ 0 are used to represent the numerical solution and when the time-stepping method is accurate with order k + 1.
Reward was delivered on an approximate Variable-Interval (VI) schedule.
The methods are devised by expressing the approximate scalar variable and corresponding flux in terms of an approximate trace of the scalar variable and then explicitly enforcing the jump condition of the numerical fluxes across the element boundary.
First, we express the approximate scalar variable and corresponding flux within each element in terms of an approximate trace of the scalar variable along the element boundary.
To approximate this variable, we will use the natural logarithm of total assets.
ExThesive newericapproximateare prescalar to demonstrate the convariable propertisshowntoe method.
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CEO of Professional Science Editing for Scientists @ prosciediting.com