Some numerical experiments with variable-storage quasi-Newton algorithms
Mathematical Programming: Series A and B
Constrained nonlinear programming
Optimization
An RQP algorithm using a differentiable exact penalty function for inequality constrained problems
Mathematical Programming: Series A and B
Representations of quasi-Newton matrices and their use in limited memory methods
Mathematical Programming: Series A and B
Large-scale SQP methods and their application in trajectory optimization
Computational optimal control
A sparse nonlinear optimization algorithm
Journal of Optimization Theory and Applications
Algorithm 630: BBVSCG–a variable-storage algorithm for function minimization
ACM Transactions on Mathematical Software (TOMS)
Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A)
Lancelot: A FORTRAN Package for Large-Scale Nonlinear Optimization (Release A)
Leveraging partially faulty links usage for enhancing yield and performance in networks-on-chip
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Sequential quadratic (SQP) programming methodsare the method of choice when solving small or medium-sized problems. Sincethey are complex methods they are difficult (but not impossible) to adapt tosolve large-scale problems. We start by discussing the difficulties that needto be addressed and then describe some general ideas that may be used toresolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT,which is intended for general applications of a particular type. These aredescribed briefly together with the ideas on which they are based. Finally wediscuss new work on developing SQP methods using explicit second derivatives.