An Algorithm for Large-Scale Quadratic Programming

An Algorithm for Large-Scale Quadratic Programming

We are particularly concerned in solving (1.1) when n is large and the vectors a, and matrix H are sparse. We do not restrict H to being positive (semi-)definite and consequently are content with finding local solutions to (1.1). Of course, for many classes of problem, it is known a priori that any local solution is a global one. Our method is fundamentally related to that proposed by Fletcher ...

Large-scale decomposition for successive quadratic programming

An efficient algorithm for large scale stochastic nonlinear programming problems

The class of stochastic nonlinear programming (SNLP) problems is important in optimization due to the presence of nonlinearity and uncertainty in many applications, including those in the field of process systems engineering. But despite the apparent importance of such problems, the solution algorithms for these problems have found few applications due to the severe computational and structural...

An Interior Point Algorithm for Large-Scale Nonlinear Programming

The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests.

An Algorithm for Solving Quadratic Programming Problems

Herein is investigated the method of solution of quadratic programming problems. The algorithm is based on the effective selection of constraints. Quadratic programming with constraintsequalities are solved with the help of an algorithm, so that matrix inversion is avoided, because of the more convenient organization of the Calculus. Optimal solution is determined in a finite number of iteratio...