Sentence examples for by introducing a slack variable from inspiring English sources

By introducing a slack variable ξ n 0, problem (21) can be reformulated as max A n 0, B n 0, ξ n 0 ξ n 0 s.t.

In order to handle the nonseparable data, the formulation is generalized by introducing a slack variable ξ for each instance y i ( w T x i + b ) ≥ 1 − ξ i, i = 1, …, n, ξ i > 0. (14).

To deal with it, we first equivalently transform it into its epigraph form by introducing a slack variable η, i.e., begin{array}{*{20}l} R tau_{ms})& =mathop{max}limits_{{mathbf{Q}_{0}},{mathbf{Q}_{c}},{mathbf{Q}_{a}},eta} C_{b}({mathbf{Q}_{c}},{mathbf{Q}_{a}} -eta text{s.t.} -eta{e,k}({matext{s.t.}}},{mathbf{Q}_{a}}) le eta, forall k in {mathcal{K}}_{e} end{array} (8a).

Furthermore, introducing a slack variable γ≥0, problem (17) is reformulated as begin{array}{*{20}l} &max_{mathbf{W},{Omega},alpha,gamma} quad gamma end{array} (18a).

Introducing a slack variable (ygeq0) and setting (g y)=0), (B=I), the above problem can be converted into Problem (1).

Previous linear matrix inequality (LMI) stability conditions are relaxed by exploring further the properties of the time derivatives of premise membership functions and by introducing a slack LMI variable into the problem formulation.

To account for the presence of outliers, we can soften the decision boundaries by introducing a slack positive variable ξ i for each training vector.

Note that there is no need to introduce a slack variable for the relay node.

By choosing an appropriate Lyapunov Krasovskii functional and introducing a slack matrix variable, a less conservative delay-dependent condition of exponential stability in mean square is presented in the form of linear matrix inequalities (LMIs).

We convert the inequality constraint of the TUMP problem (Equation 5) to the equality constraint by introducing a set of slack variables Χ, where X = {x1n+1, x2n+1,..., xmn+1}.

A free-matrix-based inequality method is developed by introducing a set of slack variables, which can be optimized via existing convex optimization algorithms.