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Therefore, instead of imposing this sparse constraint, we consider to minimize an optimization model having the ℓ 0-norm as its objective function.
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The sparse code of signal data y=y j for j=1,…,n is obtained by minimizing a constrained optimization ℓ 0: underset{leftVert xrightVert_{0}leq k}{min}=frac{1}{2}leftVert y-DxrightVert^{2}, (13).
The differences between the results obtained experimentally and computationally were minimized using an optimization technique.
NMF algorithm starts with an initial estimate for and, and performs an iterative optimization to minimize a given cost function.
NMF algorithm starts with an initial estimate for W and H, and performs an iterative optimization to minimize a given cost function.
In order to minimize health risks, an optimization step based on UV disinfection was performed.
Different from the above techniques, the smoothed l0-norm approach [13] is to approximate the discontinuous l0-norm by a suitable continuous one and then minimize it by an optimization algorithm dedicated to continuous functions.
We then minimize L using an optimization algorithm (see below, Sect. 4) and take our estimates α ˆ, β ˆ, γ ˆ to be α ˆ, β ˆ, γ ˆ = arg min α, β, γ L.
Parameter estimation is usually approached as an optimization task of minimizing an objective function that measures the goodness of fit of the model simulation to the observed data.
The EBCS codesign problem is then formulated as a linear matrix inequality (LMI) optimization problem minimizing an associated quadratic cost function.
Most of the studies on inventory control reported in earlier contributions deal with the optimization problems minimizing an expected cost criterion such as the long-run average cost.
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