This paper discusses an algorithm for efficiently calculating the control moves for constrained nonlinear model predictive control. The approach focuses on real-time optimization strategies that maintain feasibility with respect to the model and constraints at each iteration, yielding a stable technique suitable for suboptimal model predictive control of nonlinear process. We present a simulati...

We propose a sequential quadratic optimization method for solving nonlinear constrained optimization problems. The novel feature of the algorithm is that, during each iteration, the primal-dual search direction is allowed to be an inexact solution of a given quadratic optimization subproblem. We present a set of generic, loose conditions that the search direction (i.e., inexact subproblem solut...

In this paper, we prove a nonlinear separation associated with a constrained multiobjective optimization problem in the image space, as well as the equivalence between the existence of nonlinear separation function and a saddle point condition for a generalized Lagrangian function associated with the given problem. As applications, we obtain some equivalent conditions on exact penalty methods f...

Abstract We study nonlinear optimization problems with a stochastic objective and deterministic equality inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks. propose an active-set sequential quadratic programming (StoSQP) algorithm that utilizes differentiable exact augmented Lagrangian as the merit fu...

Here Θ : X → IR is a continuously differentiable mapping, X ⊆ IR is an open set containing the feasible region Ω and Ω is an n-dimensional box, Ω = {x ∈ IR : l ≤ x ≤ u}. These inequalities are meant component-wise and l ∈ (IR ∪−∞), u ∈ (IR ∪∞). Taking into account the variety of applications yielding the problem (1), we allow any relationship between m and n. The relevance of this problem is we...

We report on the theory and implementation of a global optimization solver for general constrained nonlinear programming problems based on Variable Neighbourhood Search, and we give comparative computational results on several instances of continuous nonconvex problems. Compared to an efficient multi-start global optimization solver, the VNS solver proposed appears to be significantly faster.

In this paper, we establish various results of duality for a new class constrained robust nonlinear optimization problems. For problems, involving functionals (path-independent) curvilinear integral type and mixed constraints governed by partial derivatives second order uncertain data, formulate study Wolfe, Mond-Weir dual regard, considering the concept convex vector functional, determined con...