The reduced Hessian SQP algorithm presented in 2] is developed in this paper into a practical method for large-scale optimization. The novelty of the algorithm lies in the incorporation of a correction vector that approximates the cross term Z T WY p Y. This improves the stability and robustness of the algorithm without increasing its computational cost. The paper studies how to implement the a...

Wang (1991) reported that a genetic algorithm (GA), combined with a local search method, is an efficient and robust means for the calibration of conceptual rainfall-runoff models. In this article Wang's genetic algorithm has been slightly modified in order to improve its efficiency. The "optimal parameter set" produced by the G A has then been used as the starting point for a local optimization...

Recently, nonlinear programming solvers have been used to solve a range of mathematical programs with equilibrium constraints (MPECs). In particular, sequential quadratic programming (SQP) methods have been very successful. This paper examines the local convergence properties of SQP methods applied to MPECs. SQP is shown to converge superlinearly under reasonable assumptions near a strongly sta...

In this paper, we propose an efficient zero-order algorithm that can be used to compute approximate solution robust optimal control problems (OCP) and robustified nonconvex programs in general. particular, focus on OCPs make use of ellipsoidal uncertainty sets show that, with the proposed method, efficiently obtain suboptimal, but robustly feasible solutions. The main idea lies leveraging inexa...

We propose using SQP (Sequential Quadratic Programming) to directly recover 3D quadratic surface parameters from multiple views. A surface equation is used as a constraint. In addition to the sum of squared reprojection errors defined in the traditional bundle adjustment, a Lagrangian term is added to force recovered points to satisfy the constraint. The minimization is realized by SQP. Our alg...

This paper describes a new numerical method for the design of multi-step stamping tools, in which the optimization approach is based on the Response Surface Method (RSM) with Kriging interpolation as well as the Sequential Quadratic Programming (SQP) algorithm. The present work attempts to provide a reliable methodology for the optimum design of the forming tools in order to produce a desired p...

In this paper, we introduce a constant positive linear dependence condition (CPLD), which is weaker than the Mangasarian–Fromovitz constraint qualification (MFCQ) and the constant rank constraint qualification (CRCQ). We show that a limit point of a sequence of approximating Karush–Kuhn–Tucker (KKT) points is a KKT point if the CPLD holds there. We show that a KKT point satisfying the CPLD and ...

This paper presents a general and systematic methodology, termed complete simulation-based sequential quadratic programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the sequential quadratic programming (SQP) algorithm. This approach allows the use of...

The paper deals with a global optimization algorithm using hybrid approach. To take the advantage of global search capability the evolution strategy(ES) with some modifications in recombination is used first to find the near-optimal solutions. The sequential quadratic programming(SQP) is then used to find the exact solution from the solutions found by ES. One merit of the algorithm is that the ...