Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming
Computational Optimization and Applications
A bézier-based approach to unstructured moving meshes
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
An efficient sequential quadratic programming algorithm for nonlinear programming
Journal of Computational and Applied Mathematics
Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Multivariate mixed normal conditional heteroskedasticity
Computational Statistics & Data Analysis
Proceedings of the conference on Design, automation and test in Europe
Improving solver success in reaching feasibility for sets of nonlinear constraints
Computers and Operations Research
Journal of Computational and Applied Mathematics
Computational Optimization and Applications
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Robotics and Autonomous Systems
Journal of Computational and Applied Mathematics
An efficient sequential quadratic programming algorithm for nonlinear programming
Journal of Computational and Applied Mathematics
A feasible descent SQP algorithm for general constrained optimization without strict complementarity
Journal of Computational and Applied Mathematics
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Parameter identification in financial market models with a feasible point SQP algorithm
Computational Optimization and Applications
Modifying feasible SQP method for inequality constrained optimization
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
A feasible SQP-GS algorithm for nonconvex, nonsmooth constrained optimization
Numerical Algorithms
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Interior point methods for semidefinite optimization (SDO) have recently been studied intensively, due to their polynomial complexity and practical efficiency. Most of these methods are extensions of linear optimization (LO) algorithms. As opposed to ...