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Then problem (1.1a - 1.1c 1.1a - 1.1cst one weak solution thas minimizes the function J.
Thus, we search for the value of (varvec{lambda }) that minimizes the function (F varvec{lambda })).
For any nonnegative real number, if minimizes the function, maximizes over all such that.
Hence, by the least action principle, problem (1.1) has at least one solution which minimizes the function φ in H T 1. □. Proof of Theorem 1.2.
The convex optimization problem of Eq. (8) can be solved by alternating direction method of multipliers [20], which effectively minimizes the function with iterative manner.
Similar(55)
By minimizing the function, we get the next expression for the orientation (18).
N1,..., N P is equivalent to minimizing the function, where N= [N1... N P ]T.
To minimize the function of loss circulation, two optimizing algorithms are examined.
Our first approach to optimizing stability is to directly minimize the function α(A x)).
The objective of districting is to maximize or minimize the function of the weights between different districts.
Consequently, if is a multiple of P then, the solution of minimizing the function in ℝ P coincides the solution of minimizing the function f(N) in ℕ P. Thus, the optimal placement minimizing the MSRL is.
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minimizes the morbidity
minimizes the damage
minimizes the number
minimizes the production
minimizes the packet
minimizes the stress
minimizes the amount
minimizes the need
minimizes the protocol
minimizes the strain
minimizes the within-cluster
minimizes the escape
minimizes the performance
minimizes the window
minimizes the error
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