Interior Point Methods for NLP Problems in Process Engineering

9706950比格勒本提案涉及内点法在非线性规划问题中的发展和应用。将特别关注大规模和结构化的二次规划问题，这些问题是在解决来自化学过程工程的NLP问题时遇到的子问题。建议的研究将集中于通过大规模线性代数中的有效分解方法来开发各种类型的过程工程问题的结构。此外，首席研究人员将开发和改进与连续二次规划(SQP)算法有密切相似之处的内点策略，并解决与IPMS中有效使用热启动相关的一些未决问题。最后，这些算法将被实现来展示它们在过程工程中大规模问题上的性能。日益激烈的国际竞争以及环境和资源的限制，对化工过程工业提出了更复杂的设计和制造策略。在过去的十年里，这些需求已经开始通过有效的优化战略得到解决。然而，这些问题的规模和复杂性(规模接近一百万个变量)给当前的优化算法带来了沉重的负担。拟议的研究将允许解决该行业面临的更大的优化问题。通过开发和应用非线性规划的内点方法，我们将能够为大规模的稳态和动态过程模型的优化开发出量身定制的求解策略。这将导致考虑更大和更困难的过程工程模型，以及多个过程的集成。其结果将导致对环境无害的化学过程，在将原材料转化为产品方面非常有效，在当今市场上具有很强的竞争力。

9706950 Biegler This proposal deals with the development and application of interior-point methods (IPMs) to nonlinear programming (NLP) problems. Particular attention will be devoted to large-scale and structured quadratic programming problems that are encountered as subproblems in the solution of NLPs coming from chemical process engineering. The proposed research will focus on exploiting the structure of various classes of process engineering problems through efficient decomposition methods in large-scale linear algebra. In addition, principal investigators will develop and refine an interior-point strategy that has close parallels with successive quadratic programming (SQP) algorithms and address some open questions related to the efficient use of warm-starts in IPMs. Finally, these algorithms will be implemented to demonstrate their performance on large-scale problems in process engineering. Increased international competition along with environmental constraints and resource limitations require much more sophisticated design and manufacturing strategies for chemical process industries. Over the past decade these needs have started to be addressed by efficient optimization strategies. Nevertheless, the size and complexity of these problems (with sizes approaching a million variables) impose a heavy burden on current optimization algorithms. The proposed research will allow the solution of much larger optimization problems faced by this industry. Through the development and application of interior point methods for nonlinear programming, we will be able to develop tailored solution strategies for the optimization of large-scale steady state and dynamic process models. This will lead to consideration of much larger and more difficult process engineering models, as well as the integration of multiple processes. The result will lead to chemical processes that are environmentally benign, very efficient in the conversion of raw materials to products and hi ghly competitive in today's marketplace.