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Exact(6)

In [5], the authors proposed a numerical method to solve the non-convex optimization problem (4) by considering an equivalent minimization of weighted MSE problem minimize Λ, Ω, W ~ E Ω 1 2 u - Λy 2 - log det Ω subject to Tr W ~ W ~ H ≤ P, (10).

Our approach is based on minimization of weighted l2-norm of the residuals.

Parameter estimation was done by the minimization of weighted residual squares of the conversions.

Different from [9], the approach in [5] was to establish the equivalence between the sum-rate maximization problem and the minimization of weighted sum MSE problem.

Based on iterative minimization of weighted mean-square error (MSE), a linear transceiver design algorithm for weighted sum-rate maximization has been investigated in the cellular network [3].

Results are presented for measures of array performance such as input signal power, directivity of sound radiation and accuracy of sound reproduction resulting from the application of conventional control methods such as minimization of error in mean squared pressure, maximization of energy difference and minimization of weighted pressure error and energy.

Similar(54)

For example, Krein studied in [7] the minimization problem of weighted Dirichlet eigenvalues of y ¨ + λ w ( t ) y = 0, t ∈ [ 0, 1 ].

Recently, also a limited number of energy-aware DSM designs have been proposed [7, 20, 22, 24], such as the minimization of the (weighted) sum of transmit powers subject to data rate constraints minimize s ∈ S ∑ n ∈ N t n P n subject to R n ≥ R n, target, n ∈ N, P n ≤ P n, tot, n ∈ N, (8). in which t = [t1,..., t N ] are weights.

Our approach is based on the minimization of a weighted version of the l2-norm criterion.

Our method is based on minimization of a weighted l2-norm of the prediction error.

This method is based on minimization of the weighted surface area functional over an extended domain that includes the solid phase.

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