The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve superlinear convergence in situation.
Sep 9, 2009 · Abstract The stabilized version of the sequential quadratic programming algorithm. (sSQP) had been developed in order to achieve superlinear ...
In this paper we develop a general convergence theory for a class of quasi-Newton methods for equality constrained optimization. The theory is set in the ...
The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve superlinear convergence in situations ...
The method incorporates a convexification algorithm that allows the use of exact second derivatives to define a convex quadratic programming (QP) subproblem ...
The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve superlinear convergence in situations ...
Abstract. Stabilized sequential quadratic programming (sSQP) methods for nonlinear optimiza- tion generate a sequence of iterates with fast local ...
Abstract. Stabilized sequential quadratic programming (sSQP) methods for nonlinear optimization generate a sequence of iterates with fast local convergence ...
Abstract. Regularized and stabilized sequential quadratic programming (SQP) methods are two classes of methods designed to resolve the numerical and ...
Abstract. Sequential quadratic programming (SQP) methods are a popular class of methods for nonlinearly constrained optimization.