Nonlinear programming

About: Nonlinear programming is a research topic. Over the lifetime, 19486 publications have been published within this topic receiving 656602 citations. The topic is also known as: non-linear programming & NLP.

Optimization and Coordination of Damping Controls for Improving System Dynamic Performance

Abstract: This paper presents a global tuning procedure for FACTS device stabilizers (FDS) and power system stabilizers (PSS) in a multi-machine power system using a parameter-constrained nonlinear optimization algorithm implemented in a simulation program. This algorithm deals with such an optimization problem by solving a sequential quadratic programming using the dual algorithm. The main objective of this procedure is to simultaneously optimize pre-selected parameters of the FDSs and PSSs having fixed parameters in coping with the complex nonlinear nature of the power system. By minimizing a nonexplicit target function in which the oscillatory rotor modes of the generators involved and suing characteristics between areas are included, interactions among the FACTS controls under transient conditions in a multimachine power system are improved. A multimachine power system equipped with a TCSC and an SVC as well as three PSSs is applied to demonstrate the efficiency and robustness of the tuning procedure presented. The results obtained from simulations validate the improvement in damping of overall power oscillations in the system in an optimal and globally coordinated manner. The simulations also show that the stabilizers tuned are robust in providing adequate damping for a range of conditions in the system.

Design and volume optimization of space structures

Abstract: We study the design and optimization of statically sound and materially efficient space structures constructed by connected beams. We propose a systematic computational framework for the design of space structures that incorporates static soundness, approximation of reference surfaces, boundary alignment, and geometric regularity. To tackle this challenging problem, we first jointly optimize node positions and connectivity through a nonlinear continuous optimization algorithm. Next, with fixed nodes and connectivity, we formulate the assignment of beam cross sections as a mixed-integer programming problem with a bilinear objective function and quadratic constraints. We solve this problem with a novel and practical alternating direction method based on linear programming relaxation. The capability and efficiency of the algorithms and the computational framework are validated by a variety of examples and comparisons.

A MADS Algorithm with a Progressive Barrier for Derivative-Free Nonlinear Programming

Abstract: We propose a new constraint-handling approach for general constraints that is applicable to a widely used class of constrained derivative-free optimization methods. As in many methods that allow infeasible iterates, constraint violations are aggregated into a single constraint violation function. As in filter methods, a threshold, or barrier, is imposed on the constraint violation function, and any trial point whose constraint violation function value exceeds this threshold is discarded from consideration. In the new algorithm, unlike the filter method, the amount of constraint violation subject to the barrier is progressively decreased adaptively as the iteration evolves. We test this progressive barrier (PB) approach versus the extreme barrier (EB) with the generalized pattern search (Gps) and the lower triangular mesh adaptive direct search (LTMads) methods for nonlinear derivative-free optimization. Tests are also conducted using the Gps-filter, which uses a version of the Fletcher-Leyffer filter approach. We know that Gps cannot be shown to yield kkt points with this strategy or the filter, but we use the Clarke nonsmooth calculus to prove Clarke stationarity of the sequences of feasible and infeasible trial points for LTMads-PB. Numerical experiments are conducted on three academic test problems with up to 50 variables and on a chemical engineering problem. The new LTMads-PB method generally outperforms our LTMads-EB in the case where no feasible initial points are known, and it does as well when feasible points are known. which leads us to recommend LTMads-PB. Thus the LTMads-PB is a useful practical extension of our earlier LTMads-EB algorithm, particularly in the common case for real problems where no feasible point is known. The same conclusions hold for Gps-PB versus Gps-EB.

Automated design of total water systems

Abstract: An automated method for the design of total water systems is developed in this paper. This approach considers simultaneously the optimal distribution of water to satisfy process demands and optimal treatment of effluent streams. Treatment can be for discharge to the environment or for regeneration of wastewater. The cases of regeneration reuse and regeneration recycle can be distinguished in the approach. It combines engineering insights with mathematical programming tools based on a superstructure model that results in a mixed-integer nonlinear programming problem. The approach features a fast and robust solution strategy. Complex tradeoffs involving operating, as well as capital, costs and other practical constraints have been included. In particular, piping and sewer costs, which are a major element in the capital cost of such systems, can be included. Network complexity is controlled by specifying the minimum permissible flow rates in the network, the maximum number of streams allowed at mixing juncti...

Local structure of feasible sets in nonlinear programming, Part III: Stability and sensitivity

Abstract: This paper continues the local analysis of nonlinear programming problems begun in Parts I and II. In this part we exploit the tools developed in the earlier parts to obtain detailed information about local optimizers in the nondegenerate case. We show, for example, that these optimizers obey a weak type of differentiability and we compute their derivatives in this weak sense.