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Exact(17)
Then the original optimization problem can be rewritten as follows: (37).
Next we present an example, in which a separable optimization problem can be rewritten as a split equality problem.
It is shown that the input signal design problem can be rewritten as a set of upper bound constraints and therefore solved using an existing algorithm.
The problem can be rewritten as: the search of hyperparameter values maximizing the posterior probability distribution over the hyperparameters: P(Ω|D).
After setting P to Pm−1, the formulated problem can be rewritten as max A, B R P m − 1, A, B s.t.
The optimization problem can be rewritten as follows max P k i u k s.t P k ∈ X k, com (6).
Similar(43)
Problem (32) can be rewritten as problem (30).
They are optimally chosen by solving the L2 gain minimization problem, which can be rewritten as an equivalent LMI problem.
Moreover, A has discrete spectrum with eigenvalues λ n = n2π2, n ∈ ℕ, associated normalized eigenvectors v n ( x ) = 2 sin n π x, the set {v n : n ∈ ℕ} is an orthonormal basis of H and T ( t ) u = ∑ n = 1 ∞ e - n 2 π 2 t ( u, v n ) v n, T ( t ) ≤ e - π 2 t, ∀ t ≥ 0. Let f t, u(t)) = f(·, t, u(·, t)), then the problem (24) can be rewritten into the abstract form of problem (1 - 2).
The problem (2) can be rewritten as the following equivalent constrained minimization problem minlimits_{u,d}{|d|+J u)} such that d=ϕ u).
For problem (2.2), we define an operator (K v x,cdot)rightarrow f cdot)), then problem (2.2) can be rewritten as the following operator equation: Kv x,t)=f(t),quad 0< xleq1.
More suggestions(15)
problem can be tuned
problem can be reversed
problem can be attributed
problem can be traced
problem can be considered
problem can be cracked
problem can be simplified
problem can be written
problem can be solved
problem can be corrected
problem can be remediated
problem can be avoided
problem can be compared
problem can be dealt
problem can be overstated
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