One of the most effective methods for nonlinearly constrained optimization generates steps
by solving quadratic subproblems. This sequential quadratic programming (SQP) approach …

Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has
arguably become the most successful method for solving nonlinearly constrained …

Just as in its 1st edition, this book starts with illustrations of the ubiquitous character of
optimization, and describes numerical algorithms in a tutorial way. It covers fundamental …

An n-dimensional vector x is an array of n scalars x1, x2,..., xn. The notation x represents the
array when its elements are arranged in a column, while the notation xT represents the array …

In the past decade, the control of nonlinear systems has received considerable attention in
both academia and industry. The recent interest in the design and analysis of nonlinear …

Over the last fifty years, the ability to carry out analysis as a precursor to decision making in
engineering design has increased dramatically. In particular, the advent of modern …

Fortran 77 software implementing the SPG method is introduced. SPG is a nonmonotone
projected gradient algorithm for solving large-scale convex-constrained optimization …

A sequential quadratic programming (SQP) algorithm generating feasible iterates is
described and analyzed. What distinguishes this algorithm from previous feasible SQP …

While optimality conditions for optimal control problems with state constraints have been
extensively investigated in the literature the results pertaining to numerical methods are …

In this paper, we introduce a constant positive linear dependence condition (CPLD), which is
weaker than the Mangasarian--Fromovitz constraint qualification (MFCQ) and the constant …