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Sequential Quadratic Programming (SQP) is a powerful class of algorithms forsolving nonlinear optimization problems. Local convergence of SQP algorithms isguaranteed when the Hessian approximation used in each Quadratic Programmingsubproblem is close to the true Hessian. However, a good Hessian approximationcan be expensive to compute. Low cost Hessian approximations only guaranteelocal convergence under some assumptions, which are not always satisfied inpractice. To address this problem, this paper proposes a simple method toguarantee local convergence for SQP with poor Hessian approximation. Theeffectiveness of the proposed algorithm is demonstrated in a numerical example.

A Method to Guarantee Local Convergence for Sequential Quadratic Programming with Poor Hessian Approximation