Multi-stage stochastic programming problems arise in many practical situations, such as production and manpower planning, portfolio selections and so on. In general, the deterministic equivalences of these problems can be very large, and may not be solvable directly by general-purpose optimization approaches. Sequential quadratic programming methods are very effective for solving medium-size no...

On the platform of general chemical process simulation software (it was named Optimization Engineer, OPEN), a optimization algorithm for is developed using C + code. The based on sequential quadratic programming (SQP). We adopt activity set and rotation axis to generate solve sub-problem. active method can simplify number constraints speed up calculation. At same time, we used limited memory BF...

In this paper we consider various methods for nonmetric multidimensional scaling. We focus on the nonmetric phase, for which we consider various alternatives: Kruskal’s nonmetric phase, Guttman’s nonmetric phase, monotone regression by monotone splines, and monotone regression by a monotone neural network. All methods are briefly described. We use sequential quadratic programming to estimate th...

We consider feature selection and weighting for nearest neighbor classifiers. Atechnical challenge in this scenario is how to cope with discrete update of nearestneighbors when the feature space metric is changed during the learning process.This issue, called the target neighbor change, was not properly addressed in theexisting feature weighting and metric learning literature. I...

This paper presents an approach to compute the neighboring extremal solution for an optimal switched impulsive control problem with a pre-specified sequence of modes and a large perturbation in the initial state. The decision variables – the subsystem switching times and the control parameters – are subject to inequality constraints. Since the active status of these inequality constraints may c...