Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging task for the academic research currently is to address large-scale, complex optimization problems under various uncertainties. Therefore, investigations on the development of stochastic optimization approaches are necessi...

Factor Analysis (FA) is a technique of fundamental importance that is widely used in classical and modern multivariate statistics, psychometrics, and econometrics. In this paper, we revisit the classical rank-constrained FA problem which seeks to approximate an observed covariance matrix (Σ) by the sum of a Positive Semidefinite (PSD) low-rank component (Θ) and a diagonal matrix (Φ) (with nonne...

In this paper we consider the problem of solving a special class of nonlinear constrained optimization problems. The study of this class of problems has been motivated by a practical application, namely the railway yield management problem. The aim of this paper is to deene a nonlinear minimization algorithm which exploits as much as possible the structure of the problems under consideration. T...

This paper is mainly devoted to the study of the so-called full Lipschitzian stability of local solutions to finite-dimensional parameterized problems of constrained optimization, which has been well recognized as a very important property from both viewpoints of optimization theory and its applications. Based on secondorder generalized differential tools of variational analysis, we obtain nece...

In this report we describe a comparison of different algorithms for solving nonlinear optimization problems with simple bounds on the variables. Moreover, we would like to come out with an assessment of the optimization library DOT used in the optimization suite OPTALIA at Airbus for this kind of problems.

This paper presents an effective optimization method for nonlinear constrained optimization of retaining structures. The proposed algorithm is based on the particle swarm optimization with passive congregation. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the retaining wall. To applying the constraints, the algorithm e...

Linear programming problem is widely applied in engineering group. And artificial neural network is an effective and practical method and approach for solving linear programming problem of nonlinear convex set constraints in engineering field. Most models of artificial neural network are nonlinear dynamic system. If the objective function of optimization calculation problem is corresponding to ...

Some connections between constructive real algebraic geometry and constrained optimization are exploited. We show how the problem of determining the projection of a real-algebraic variety on a certain axis is equivalent to a problem in nonlinear programming. As an application, Grr obner bases are used to deal with an optimization problem arising in the theory of local Lyapunov functions. The pr...

In this paper, we review the trust region algorithms for nonlinear optimization. The philosophy and the fundamental ideas of trust region algorithms are discussed. Model algorithms for unconstrained optimization, constrained optimization, and nonsmooth optimization are given. Main techniques for global convergence and local superlinear convergence are analyzed.

We establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are considered. Numerical simulations for three constrained nonlinear optimization problems are giv...