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After that, the link reliability value is updated by multiplying the calculated value and the saved value in RREQ message according to Equation (19).
In the time-stepping process of the proposed method, the column vector containing the spatially sampled field values is updated by multiplying it with an iteration matrix, which is written in a matrix-exponential form.
The forward message is updated by multiplying with the output of the normal probability distribution function NORMAL_PDF (implemented as in (19)) which takes the cost and noise standard deviation as input.
For each (k,m) pair, the forward message is initialized to zero, after which the message is updated by multiplying and accumulating messages from the previous time-step k−1.
Proof Using Proposition 4.14 with γ : = 1 2 and λ : = 1 4 ( 1 + 2 ) ( 1 + 2 κ ) σ ( log p ) 3 2, we obtain the number of inner iterations 4 2 ( 2 + 2 ) ( 1 + 2 κ ) σ ( log p ) 3 2 Ψ ¯ 0 1 2. If the central path parameter μ has the initial value μ 0 > 0 and is updated by multiplying 1 − θ, 0 < θ < 1, then after at most ⌈ 1 θ log n μ 0 ϵ ⌉ iterations we have n μ < ϵ [18].
Therefore, the value of μs'500nm is updated by multiplying the current value of μs'500nm by the ratio 0.65/W1.
Similar(53)
In any iteration, each node updates its solution vector by multiplying the stochastic weight matrix by the summation of its neighbors' parameters and the sub-gradient of the objective function.
Fading factors with incremental algorithms can be implemented by multiplying the similarity matrix by a factor α<1 before each update.
Start by multiplying your ounces by 30.
Training the neural networks with the weight decay means that during the training phase, each updated weight is multiplied by a factor slightly less than 1 to prevent the weight from growing too large.
We then multiply the updated risk by the RR to get the revised product-based estimator.
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