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The optimal solution is found by solving a sequence of problems related by a continuation parameter.
The expansion terms can be determined by solving a sequence of equations.
The filter parameters can be calculated by solving a sequence of linear matrix inequalities.
In Algorithm 1, we will solve the BVI (1 - 2) by solving a sequence of the approximation problem PVI (8).
Accordingly, a gain-switched state feedback controller can be obtained by solving a sequence of linear matrix inequalities (LMIs) based optimization problems.
First, they are found automatically by solving a sequence of optimization problems, which eliminates the problem of selecting a wavelet for a given signal.
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Here, we show that (16) can be solved by alternatively solving a sequence of the min-max power problems.
Moreover, we develop a low-complexity algorithm for this problem by iteratively solving a sequence of convex subproblems.
We show that such an approximate solution can be found by approximately solving a sequence of sample average approximations.
By solving a limited sequence of sub-problems, solutions are found that rigorously meet the first excursion constraints of the original problem.
The sequence selection stage produces a rank-ordered list of amino-acid sequences by solving an integer programming sequence selection model.
More suggestions(15)
by solving a literature
by solving a revenue
by solving a optimization
by solving a missile
by solving a linear
by solving a difference
by solving a benchmark
by solving a sub-problem
by solving a parameter-dependent
by solving a min-max
by performing a sequence
by solving a minimization
by solving a series
by solving a system
by solving a number
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com