General Nonlinear Optimization Problems


The most general class of optimization problems is the class of problems where both the objective function and the constraints are nonlinear, as formulated in Eq. (10.1). These problems can be solved by using a variety of methods such as penalty- and barrier-function methods, gradient projection methods, and sequential quadratic-programming (SQP) methods [1]. Among these methods, SQP algorithms have proved highly effective for solving general constrained problems with smooth objective and constraint functions [2]. A more recent development in nonconvex constrained optimization is the extension of the modern interior-point approaches of Chaps. 12–14 to the general class of nonlinear problems.


Line Search Feasible Region Merit Function Apply Algorithm Nonconvex Problem 
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