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Therefore, the problems (25)–(26) are nonlinear programming problems which can be simply solved by a nonlinear programming solver.
The problem is to be relaxed into an equivalent nonlinear optimization problem such that it can be solved by a nonlinear programming (NLP) solver.
The optimization problem is relaxed into an equivalent nonlinear optimization problem such that it can be solved by a nonlinear programming (NLP) solver.
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In this manner the short-term line ampacity is obtained by solving a nonlinear programming problem.
Furthermore, an optimal weighted transient performance is obtained by solving a nonlinear programming problem.
The design of such an observer is obtained by solving a nonlinear programming problem and using series expansions to set the time-varying gain.
In addition, the local contact deformations of the workpiece fixture system are determined by solving a nonlinear programming problem of minimizing the total complementary energy of the frictional workpiece fixture subsystem in machining system.
The optimization is achieved by solving online a nonlinear programming problem which considers the relation between the HPR and the organic loading rate (OLR), such that the latter is manipulated to maximize the former.
The optimization is a nonlinear programming problem and solved by stochastic algorithm of particle warm optimization.
Optimal control of systems is formulated as a nonlinear programming problem and solved by the sequential quadratic programming method.
The numerical resolution of a nonlinear programming problem is standard, by gradient methods, penalization, quasi-Newton, dual methods, etc. (see, e.g., [9, 50, 57, 81]).
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