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...

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 research, using the Modified Compression Field Theory (MCFT), a new model is proposed to predict the behavior of reinforced concrete coupling beams. The model is able to consider the effects of shear and axial forces and bending moment, simultaneously. The proposed model includes a nonlinear shear spring, an axial spring and some nonlinear springs for considering bending deformations. I...

In this paper, an optimal control of quadratic performance index with nonlinear constrained is presented. The sine-cosine wavelet operational matrix of integration and product matrix are introduced and applied to reduce nonlinear differential equations to the nonlinear algebraic equations. Then, the Newton-Raphson method is used for solving these sets of algebraic equations. To present ability ...

We present an extension, for nonlinear optimization under linear constraints, of an algorithm for quadratic programming using a trust region idea introduced by Ye and Tse [Math. Programming, 44 (1989), pp. 157–179] and extended by Bonnans and Bouhtou [RAIRO Rech. Opér., 29 (1995), pp. 195–217]. Due to the nonlinearity of the cost, we use a linesearch in order to reduce the step if necessary. We...

A novel nonlinear programming (NLP) strategy is developed and applied to the optimization of diierential algebraic equation (DAE) systems. Such problems, also referred to as dynamic optimization problems, are common in chemical process engineering and remain challenging applications of nonlinear programming. These applications often consist of large, complex nonlinear models that result from di...