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The key to solving (1) relies on a natural interpolation between the two constraints (N_{1}) and (N_{2}).
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Based on the results, it is concluded that depending upon the constraint conditions, the double-constraint optimization problem can be simplified to solving either one of the single-constraint problems or directly finding the intersection points between the two constraint boundaries.
The multiple linear regression method was adopted to work out the quantitative relationships between the five constraints and fracture density, and the fracture density property of the whole area was calculated.
To strike a good balance between these two constraints, we propose to choose the processing window for each classification algorithm according to the rate of improvement of the classification performance with respect to the processing window.
Therefore, it is natural to expect that the system would try to optimize between these two constraints.
Conventional clinical methods very often fail to find right drug doses that balance between these two constraints due to their inherent conflicting nature.
In addition to AE and subchannel limitations, we also apply the constraint derived from Equation 2 into the power limitation where we choose the minimum value between the two power constraints P i (maximum transmission power limitation) and p c -P(d i ).
Smoothness of the transition is implied by imposing the following constraints for g : g = β 0 + β 1 τ g = β 2 + β 3 (∂ ∂ t g ) = β 1 (∂ ∂ t g ) = β 3. The top two constraints imply continuity between the polynomial curve and the two linear parts, and the bottom two constraints imply smoothness at the points where the polynomial curve connect to the two linear parts.
The other skeletal shapes literally vary between the two main functional constraints: suction feeding and grazing/biting.
Grey dashed lines represent (approximate) fronts of constant global flexibility due to tradeoff between two constraints types.
But the other two constraints are real.
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