Sequential quadratic programming (SQP) methods have proved highly effective for solving
constrained optimization problems with smooth nonlinear functions in the objective and …

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 …

Nonlinear programming (NLP) has become an essential tool in process engineering,
leading to profit gains through improved plant designs and better control strategies. The …

The sequential quadratic programming (SQP) algorithm has been one of the most
successful general methods for solving nonlinear constrained optimization problems. We …

Recent developments in numerical methods for solving large differentiable nonlinear
optimization problems are reviewed. State-of-the-art algorithms for solving unconstrained …

This paper considers numerical algorithms for nding local minimizers of metric
multidimensional scaling problems. Both the STRESS and SSTRESS criteria are …

The aim of this book is to present the mathematical theory and the know-how to make
computer programs for the numerical approximation of Optimal Control of PDE's. The …

In this paper, we propose a BFGS trust-region method for solving symmetric nonlinear
equations. The global convergence and the superlinear convergence of the presented …

We compare and contrast a number of recent sequential quadratic programming (SQP)
methods that have been proposed for the solution of large-scale nonlinear programming …