Abstract. In the vicinity of a solution of a nonlinear programming problem at which both strict complementarity and linear independence of the active constraints may fail to hold, we describe a technique for distinguishing weakly active from strongly active constraints. We show that this information can be used to modify the sequential quadratic programming algorithm so that it exhibits superli...

Constrained nonlinear programming problems often arise in many engineering applications. The most well-known optimization methods for solving these problems are sequential quadratic programming methods and generalized reduced gradient methods. This study compares the performance of these methods with the genetic algorithms which gained popularity in recent years due to advantages in speed and r...

Empirical likelihood ratio method (Thomas and Grunkmier 1975, Owen 1988, 1990, 2001) is a general nonparametric inference procedure that has many nice properties. Recently the procedure has been shown to also work with censored data with various parameters and the nice properties also hold there. But the computation of the empirical likelihood ratios with censored data and/or complex setting is...

Dimethyl ether (DME) is of great industrial interest due to its use as clean fuel for diesel engines or in combustion cells, as a precursor to other organic compounds, as well as a green aerosol propellant that can effectively replace chloro-fluoro-carbons. Conventionally, high purity DME is synthesized by dehydration of methanol produced from syngas, in a process involving a catalytic fixed-be...

This paper proposes a model predictive control (MPC) strategy for a differential-drive mobile robot. By using MPC, an appropriate optimal control law is implicitly obtained. Furthermore, the system physical constraints on state and inputs are dealt with in a straightforward way. The optimization problem is solved by sequential quadratic programming (SQP) and experimental results show that the c...

A parallel, filter-based, sequential quadratic programming (SQP) algorithm is implemented and tested for typical general-purpose engineering applications. Constrained engineering test problems, including a finite element simulation, with up to 512 design variables are considered. The accuracy and serial performance of the filter-based algorithm are compared against that of a standard SQP algori...

Imaging of dielectric cylinders buried in a known inhomogeneous medium based on nonradiating (NR) current reconstruction is presented. For this, we use the finite element-boundary integral (FE-BI) method to formulate the scattering problem, then, we modify the subspace optimization method (SOM) by using sequential quadratic programming (SQP) for solving inverse scattering problem. Also the effe...

This paper reviews, extends and analyzes a new class of penalty methods for nonlinear optimization. These methods adjust the penalty parameter dynamically; by controlling the degree of linear feasibility achieved at every iteration, they promote balanced progress toward optimality and feasibility. In contrast with classical approaches, the choice of the penalty parameter ceases to be a heuristi...

The goal of this paper is to solve Mathematical Program with Complementarity Constraints (MPCC) using nonlinear programming (NLP) techniques. This work presents two algorithms based on several nonlinear techniques such as Sequential Quadratic Programming (SQP), penalty techniques and regularization schemes. A set of AMPL problems were tested and the computational experience shows that both algo...

In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function model predictive control (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternat...