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Therefore, the maximization problem can be simplified.
Specifically, the optimization problem can be simplified to find (17).
However, such a problem can be simplified by using the so-called Rosseland approximation (Rosseland, 1936).
The problem can be simplified by replacing a group of variables with a single new variable.
Through the integer constraint relaxation, the MINLP problem can be simplified into a convex optimization problem.
The problem can be simplified as min_{{beta_{i}^{n}}, rho_{i}^{m,n} forall i} bar{P} left(boldsymbol{beta}^{n}, boldsymbol{rho}^{m,n}right) (23).
Similar(26)
The formulation of such problems can be simplified by using specialized equations which model heat conduction in rigid shells in terms of two temperature fields: one for the average temperature and the other for the average temperature gradient through the shell's thickness.
Frequently graph theoretical problems can be simplified if some restrictions are imposed on the graph.
The proof of Lemma 4is provided in Appendix 4. As a consequence of Lemma 4, problem P4 can be simplified into the power allocation problem from slot 0 to slot T 2, which is labeled as P5. ( P5 ) maximize ∑ t = 0 T / 2 g ( P ( t ) ) (23a) subject to ∑ t = 0 T / 2 P ( t ) = T 2 + 1 P av, (23b) variables P ( t ) ≥ 0, ∀ t ∈ [ 0, T / 2 ], (23c).
It is shown that by the adaptation of the performance functional in a specially constructed equivalent hierarchical optimization problem, calculations can be simplified.
PAST interprets the signal subspace estimate as the solution of a minimization problem which can be simplified via an appropriate projection approximation and then applies a recursive least squares (RLS) procedure to track the signal subspace.
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CEO of Professional Science Editing for Scientists @ prosciediting.com