In 2020, Yamakawa and Okuno proposed a stabilized sequential quadratic semidefinite programming (SQSDP) method for solving, in particular, degenerate nonlinear optimization problems. The algorithm is shown to converge globally without constraint qualification, it has some nice properties, including the feasible subproblems, their possible inexact computations. convergence was established approx...

Abstract We study nonlinear optimization problems with a stochastic objective and deterministic equality inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks. propose an active-set sequential quadratic programming (StoSQP) algorithm that utilizes differentiable exact augmented Lagrangian as the merit fu...

We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. assume for the that its evaluation, gradient, Hessian are inaccessible, while one can compute their estimates by, example, subsampling. propose algorithm based on sequential quadratic programming (SQP) uses differentiable exact augmented Lagrangian as merit function. To motiva...

We provide an eeective and eecient implementation of a sequential quadratic programming (SQP) algorithm for the general large scale nonlinear programming problem. In this algorithm the quadratic programming subproblems are solved by an interior point method that can be prematurely halted by a trust region constraint. Numerous computational enhancements to improve the numerical performance are p...

The utility of numerical codes is greatly enhanced if they can be used in design, a situation that typically involves iterative optimization algorithms. An attractive way is to use gradient-based algorithms developed for solving nonlinear programming (NLP) problems. In this letter, we examine the performance of a general sequential quadratic programming (SQP) optimization algorithm for designin...