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With this formulation, the minimization of the disturbance effect on the residual is formulated as a standard H∞ filtering problem and the design is solved using standard H∞ techniques.
With this formulation, the minimization of the disturbance effect on the residual is formulated as a standard H∞ filtering problem and the design is solved using an algebraic Riccati equation.
The filter design is formulated as a standard optimization problem with linear matrix inequality (LMI) constraints.
The resulting optimization problem is formulated as a standard second-order cone programming problem and solved by the optimization toolbox Mosek.
In the second part, the design problem is formulated as a standard H∞-model matching problem which is solved using H∞-optimization LMI techniques.
The power allocation problems for sum-rate maximization on Gaussian cognitive MAC under mutual interferences between the PU and the SU communications are formulated as a standard non-convex quadratically constrained quadratic problem (QCQP) where semidefinite relaxation (SDR) has been applied to find a simple solution.
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The coupled problem is formulated as a symmetric standard problem.
Optimization is an approach that iteratively improves the performance of a system, which is formulated as a single standard measurement equation called cost (or objective) function.
With Nataf transformation, P-OPF problem is formulated as a multiple integral over standard normal space.
In these case studies, the problem is formulated as a VRP with time windows (VRPTW) and standard local search heuristics are then applied.
Like standard SVMs it is formulated as a quadratic program in which the data from an arbitrary space χ only appears in an inner product in the dual formulation and thus can be replaced by any Mercer kernel k: χ × χ → ℜ.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com