Make a QP approximation to the original problem. For the first iteration, use a Lagrangian Hessian equal to the identity matrix. Solve for the optimum to the QP problem. As part of this solution, ...

The implementation is based on the fminslp framework. However, instead of relying on Sequential Linear Programming (SLP), the fminsqp framework relies on Sequential Quadratic Programming (SQP). In ...

This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is ...

Abstract: This paper reviews the integrated perturbation analysis - sequential quadratic programming (IPA-SQP) approach. The IPA-SQP approach has been proposed to address computational challenges in ...

This study presents a hybrid algorithm obtained by combining a genetic algorithm (GA) with successive quadratic sequential programming (SQP), namely GA-SQP. GA is the main optimizer, whereas SQP is ...

Abstract: This paper reviews the integrated perturbation analysis - sequential quadratic programming (IPA-SQP) approach. The IPA-SQP approach has been proposed to address computational challenges in ...

State Key Lab of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China College of Chemical Engineering and ...

where f: n ® is a C 2 nonlinear function, C: n ® m represents a set of C 2 nonlinear constraints and we suppose that -¥ < l i < u i < ¥, for i = 1,...,n. Naturally, some of the components of x in (1) ...