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In the scheme, a trade-off between the amount of spectrum overshooting and sidelobe suppression without increasing the computational complexity is obtained by solving the optimizing problem in AIC; the influence of spectrum overshooting can be totally removed.
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At each time interval the flux distribution, v, which optimizes the growth rate μ is calculated by solving the optimization problem described in Equations 1, 2, 3 and 6.
After introducing a pair of differential equations that are expected to be valid for the optimized design, the grid thickness is optimized by solving the boundary value problem of coupled differential equations.
For given source matrix B satisfying (11), the relay matrices {F i } are optimized by solving the following problem: min { F i } tr I N b + H ~ H C ~ - 1 H ~ - 1 (13) s.t.
Remark 2. Notice that when other variables are fixed, can be optimized by solving the Karush-Kuhn-Tucker (KKT) conditions, where the Lagrangian multiplier that arises due to the relay power constraint can be obtained by using the bisection algorithm like in [15].
In that view, the measurement configurations (boldsymbol {q}^{mathrm{meas}}) can be optimized by solving the optimum experimental design (OED) problem begin{aligned}& min_{ boldsymbol {q}^{mathrm{meas}}} phibigl( {C}bigl( boldsymbol {p}, boldsymbol {q}^{mathrm{meas}}bigr bigr) end{aligned} (7a) begin{aligned}& quad text{s.t.
With a objective function and different pumping powers, five critical parameters (the fiber length, L; the proportion of pump power for pumping Nd3+, η; Nd3+ and Yb3+ concentrations, NNd and NYb and output mirror reflectivity, Rout) of the given NYDFLs are optimized by solving the rate and power propagation equations.
By solving the problem of (15), they achieved to optimize the projection matrix.
Our future study is to optimize the results obtained, by solving the node increase problem and to incorporate the framework into cloud computing technology.
By assuming that SIC decoder is available or not available at the SBS, we propose an iterative optimization-based scheme to solve the optimization problem by iteratively optimizing data traffic offloading, time, and power allocation.
The noncausal filter Q q) and learning function L q) are simultaneously optimized by solving a convex optimization problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com