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Zeroth, first and second order perturbations are computed using a spectral approach in which a system reduction scheme to the modal subspace expanded by the deterministic linear eigenmodes and equivalent linearization with Gaussian closure are applied.
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The resulting deterministic linear equations are then solved by using the familiar techniques like the Gauss elimination method or the Gauss Seidel iteration method.
Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.
Estimation problems in the presence of deterministic linear nuisance parameters arise in a variety of fields.
The ECMP and MCMP problems are deterministic linear zero-one problems which can be solved using several methods proposed by the literature such as in [25, 26].
This interaction is governed by the linear, deterministic Schrödinger equation, and is represented in the following terms.
As noted earlier, the idea of time flows with deterministic, linear past and non-deterministic, branching future has a venerable history; we have already mentioned the different views associated with Ockham and Peirce (Section 1).
Moreover, due to noise and nonlinear interaction, deterministic linear terms appear in the amplitude equation.
Another advantage of the ssLNA is that it enables quick computation of noise statistics through the solution of a set of simultaneous, deterministic linear algebraic equations.
Thus, the finally obtained linear model under uncertainty can be further converted to deterministic linear models, which can be solved by conventional algorithms.
The algorithm is summarized in Algorithm 1. PLDA was presented by Prince and Elder in [10] as a probabilistic estimation for deterministic linear discriminant analysis (LDA) [28].
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