Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: The methods considered cover the following areas: tailoring of nonlinear programming algorithms to the structure of the online optimization, use of the optimal control formulation of the receding horizon problem, constraint and cost approximations based on state space partitioning, and reparameterization of the degrees of freedom in predictions.
153 citations
TL;DR: An equivalent multi objective linear programming form of the problem has been formulated in the proposed methodology using fuzzy set theory approach and the proposed solution procedure has been used to solve numerical examples.
153 citations
TL;DR: This work first linearizes the model, and then provides a Lagrangean heuristic that finds high-quality solutions within reasonable computational time that provides new and realistic insights into the hub-and-spoke network design problem.
153 citations
TL;DR: This paper proposes a method which uses nonlinear optimization and is based on direct differentiations of value functions and is then applied to general switched linear quadratic (GSLQ) problems.
Abstract: This paper presents an approach for solving optimal control problems of switched systems. In general, in such problems one needs to find both optimal continuous inputs and optimal switching sequences, since the system dynamics vary before and after every switching instant. After formulating a general optimal control problem, we propose a two stage optimization methodology. Since many practical problems only concern optimization where the number of switchings and the sequence of active subsystems are given, we concentrate on such problems and propose a method which uses nonlinear optimization and is based on direct differentiations of value functions. The method is then applied to general switched linear quadratic (GSLQ) problems. Examples illustrate the results.
153 citations
TL;DR: In this paper, a transmission operator operating under different incentives decides about investment in transmission network while anticipating the outcome of a purely competitive electricity market where several rival generation firms complete with each other to determine operating level of their generators, amount of their sales and amount of investment in generation capacity.
Abstract: The main purpose of this paper is to develop methodologies for simultaneous generation and transmission expansion planning problem of power networks while investigating interactions between these two important sections. In the proposed methodologies, a transmission operator (TO) operating under different incentives decides about investment in transmission network while anticipating the outcome of a purely competitive electricity market where several rival generation firms complete with each other to determine operating level of their generators, amount of their sales and amount of their investment in generation capacity. The proposed methodologies rely on bi-level programming models whose upper level model represent the problem of investment in transmission by TO and the lower level problems represent market outcomes obtained from clearing the market. These bi-level models are reduced to mixed-integer linear and nonlinear programming using the duality theory and Karush-Kuhn-Tucker (KKT) optimality conditions. The results of the proposed models are analyzed and compared using two illustrative examples.
153 citations