Distributed optimization using multiple computing agents in a localized and coordinated manner is promising approach for solving large-scale problems, e.g., those arising model predictive control (MPC) of plants. However, distributed algorithm that computationally efficient, globally convergent, amenable to nonconvex constraints remains an open problem. In this paper, we combine three important...

A large mixed-integer linear program (MILP) and a much smaller nonlinear programming (NLP) approximation of the MILP, involving simulation and response surface estimation via regression analysis, are proposed to solve the problem of the optimal selection of natural gas supply contracts by local gas distribution utilities. Each potential supply source is characterized by several price and nonpri...

The problem of optimal experimental design (OED) for parameter estimation of non-linear dynamic systems is considered. It is shown how this problem can be formulated as a dynamic optimization (optimal control) problem where the performance index is usually a scalar function of the Fisher information matrix. Numerical solutions can be obtained using direct methods, which transform the original p...

We describe a software environment for dynamic optimization using the simultaneous approach. Over the past few years, there has been significant evelopment in the formulation of simultaneous dynamic optimization problems, using collocation on finite elements, and in the solution of the esulting large-scale nonlinear programming problem, using powerful barrier NLP solvers. Here we describe the b...

Fuel cells, process heat integration and open gas turbine electricity cogeneration can be optimized simultaneously using nonlinear programming (NLP) algorithm. The NLP model contains equations of structural and parametric optimization and is used to optimize complex and energy intensive continuous processes. The procedure does not guarantee the global cost optimum, but it does lead to good, per...

A multiple target track estimation method that operates directly from array data is presented. The maximum a-posteriori (MAP) estimator for contact states is derived for temporally uncorrelated signals and uncorrelated contact tracks, where the number of contacts is assumed known. This estimator is an iterative algorithm employing a nonlinear programming (NLP) penalty method in conjunction with...

Abstract. This paper pr esents an energ y-optimal trajectory planning method fo r spacecraft fo rmation reconfiguration in deep space environment using continuous lo w-thrust propulsion system. First, we emplo y the Legendre pseudospectral method (LPM) to transform the optimal reconfiguration problem to a parameter optimization nonlinear programming (NLP) problem. Then, to avoid the computation...

This paper presents a framework that unifies various search mechanisms for solving constrained nonlinear programming (NLP) problems. These problems are characterized by functions that are not necessarily differentiable and continuous. Our proposed framework is based on the first-order necessary and sufficient condition for constrained local minimization in discrete space that shows the equivale...