In that case, the EVM optimization can be formulated as minimize p p ∈ ℝ, c ∈ ℂ N (16).
In [14], we showed that the optimal node degree allocations problem can be formulated as Minimize ∑ i = 1 M a i d v i Subject to ∑ i = 1 M a i ⋅ d v i - J ( d v i - 1 ) ⋅ J - 1 ( I e, D e m, w ) 2 + J - 1 ( I e, C n d, w ) 2 + I a, D e m, w + ε w ≤ 0 ( for 1 ≤ w ≤ N ) and ∑ i = 1 M a i = 1 (14).
If channel vectors h k 's are known exactly at the BS (perfect CSIT), the problem can be formulated as minimizing total transmission power subject to SINR constraints (non-robust formulation) min g k ′ s ∑ k = 1 K ∥ g k ∥ 2 s.t.
By modeling the CSIT errors as elliptically bounded uncertainty regions, this problem can be formulated as minimizing the transmission power subject to the worst-case signal-to-interference-plus-noise ratio constraints.
For such a system, a popular measure of quality of service (QoS) is the signal-to-interference-plus-noise ratio (SINR)[1] and optimal downlink transmit beamforming can be formulated as minimizing the transmission power subject to the SINR constraints.
In watermark decoding, the embedded hidden message should be decoded accurately at the receiver side and therefore the bit error rate is usually used as the performance criterion to measure the accuracy of the decoder in extracting the hidden message, and the watermark decoding problem can be formulated as minimizing the bit error rate.
Basically, the quadratic programming problem can be formulated as: minimizing f(x) = 1/2 x T C x+ c T x with respect to x, with linear constraints Ax ≤ a,which shows that every element of the vector Ax is ≤ to the corresponding element of the vector a.
The rate parameter estimation problem can naturally be formulated as minimizing a sum of squared errors (SSE), where each error is a difference between simulated concentration and observed concentration, and the summation is over time points and/or experimental treatments.
A multi-stage probabilistic TEP model is formulated as minimizing the total cost with the chance constrained index ɛ t proposed in Section 2.1, as follows.
Our multi-stage planning objective is formulated as minimizing the total cost, including investment cost, operating cost, emission cost and a risk factor of load curtailment.
Two optimization problems are formulated as minimizing maximum per-user mean square error (MSE) and sum MSE with the per-transmitter power constraint.
