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angular uncertainty, we consider a robust problem formulation in the following.
Though they called the growth uncomfortable, participants recognized that the MakerSpace and interprofessional aspects allowed for more robust problem solving.
As we proceed, we may still obtain the results that robust problem (6) can be reformulated as QCQP or convex program under suitably defined uncertain sets.
Sun et al. [19] also investigated the robust problem in cognitive radio networks to minimize the total power consumption of SU-Tx under the QoS constraint at SU-Rx and the interference constraint at PU-Rx.
We now prove that robust problem (6) can be reformulated as a quadratically constrained quadratic program (QCQP) or convex program with a conic quadratic inequality quadratic program [17, 18] under the different uncertain sets.
Having explained the problem without ambiguity, this will set the stage to explain the robust problem with model uncertainty thereafter in the main Section"Good-deal hedging and valuation under combined uncertainty".
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The numerical results of a bi-spar wing show its effectiveness and low computational time in dealing with the robust problems with mass and stiffness perturbations.
Then the robust optimization problem is simplified to a bilinear programming problem based on duality theory.
The worst-case robust design problem was first converted into a SI-SRINR problem.
Numerical examples involving stochastic inverse heat conduction problems, contamination source identification problems and large deformation robust design problems are discussed.
It guarantees the feasibility for the robust optimization problem.
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