Sequential quadratic programming (SQP) methods are known to be efficient for solving a series of related nonlinear optimization problems because of desirable hot and warm start properties–a solution for one problem is a good estimate of the solution of the next. However, standard SQP solvers contain elements to enforce global convergence that can interfere with the potential to take advantage o...

An optimal control problem governed by a bilinear elliptic equation is considered. This problem is solved by the sequential quadratic programming (SQP) method in an infinite-dimensional framework. In each level of this iterative method the solution of linear-quadratic subproblem is computed by a Galerkin projection using proper orthogonal decomposition (POD). Thus, an approximate (inexact) solu...

We compare and contrast a number of recent sequential quadratic programming (SQP) methods that have been proposed for the solution of large-scale nonlinear programming problems. Both line-search and trust-region approaches are considered, as are the implications of interior-point and quadratic programming methods. 1 Presented at the 19th IFIP TC7 Conference on System Modelling and Optimization,...

We consider a combined IPM-SQP method to solve smooth nonlinear optimization problems, which may possess a large number of variables and a sparse Jacobian matrix of the constraints. Basically, the algorithm is a sequential quadratic programming (SQP) method, where the quadratic programming subproblem is solved by a primal-dual interior point method (IPM). A special feature of the algorithm is t...

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...

Optimization problems constrained by nonlinear partial differential equations have been the focus of intense research in scientific computing lately. Current methods for the parallel numerical solution of such problems involve sequential quadratic programming (SQP), with either reduced or full space approaches. In this paper we propose and investigate a class of parallel full space SQP Lagrange...

A minimum-time Model Predictive Control (MPC) problem is considered. By employing a time scaling transformation and cost regularization, it is shown that this problem becomes amenable to the application of parametric Integrated Perturbation Analysis Sequential Quadratic Programming (IPA-SQP). The IPA-SQP framework exploits neighboring extremal optimal control and sequential quadratic programmin...

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...

By enabling constraint-aware online model adaptation, predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world applications and received considerable attention the learning-based community. Yet, solving resulting optimal problem real-time generally remains a major challenge, due to i) increased number of augmented states optimization problem, ...

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic environmental dimensions of sustainability. This paper presents a novel problem for stock management reusable products in single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number orders, etc., are considered their stochast...