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The resulting synthesis problem is recast as an optimization over linear matrix inequalities (LMIs).
In this paper, the nonlinear problem is recast as a system of algebraic equations.
The bi-level problem is recast into its equivalent single level problem driven by Karush-Kuhn-Tucker optimality conditions.
Using warping function, this problem is recast as a convex quadratic optimization problem, which is tractable both theoretically and practically.
First, the original problem is recast as a perfect state information problem by complementing the nonlinear system with a suboptimal state estimator based on a bank of extended Kalman filters.
The NPR metamaterial design problem is recast as a variational problem, where the effective elastic properties of the spatially periodic microstructure are formulated as the strain energy functionals under uniform displacement boundary conditions.
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The method of numerically integrating two-point asymptotic boundary value problem of the boundary layer type, the initial value method, requires that the problem be recast as an initial value problem.
As explained above (see "The frame problem"), the frame problem can be recast as a problem of relevance.
In Section 3, the problem at hand is recast as a sparse linear regression, and the novel group Lasso approach is introduced.
Soft constraint formulation The problem ((mathcal {P}_{A})) is recast as an unconstrained optimal control problem by adding a penalty function to the cost functional defined by (34).
However, if the control objective and the constraints depend monotonically on the outputs of the system, the model predictive control problem can be recast as problem with a convex feasible set.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com