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The proposed method is guaranteed to find the optimum solution.
The ADMM method is guaranteed to converge for all values of its penalty parameter ρ[21].
Furthermore, for the first time, the proposed method is guaranteed to converge to global optimal settings.
The method is guaranteed to find all feasible controllers of given structure in the search domain.
Since f(s) is convex [31], a gradient search method is guaranteed to converge to a global optimum.
The synthesis filter obtained by the current method is guaranteed to be stable, which makes the method superior to many of its alternatives.
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Numerical stability and robustness of the proposed method are guaranteed by an adaptive procedure for progressively relaxing the penalized Heaviside function associated with the formulation of the discrete variational inequality.
Various approaches have been proposed [ 20, 34, 35], but since the range and distribution of network weights is method-dependent, a fair comparison of methods is guaranteed only for individually optimized thresholds, for example, maximized F1 score.
With the improved Wolfe line search, the global convergence of Dai-Kou type conjugate gradient methods was guaranteed.
One of the well-known results demonstrates that the sparsity-based methods such as ℓ1 minimization and greedy methods, are guaranteed to perfectly recover the s-sparse vectors when μ Φ < 1 2 s [6].
We provide a discussion of when random search methods are guaranteed to converge almost surely to a globally optimal system design, as well as a description of desirable features such methods should have in order to yield attractive empirical performance.
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