US-France Cooperative Research (INRIA): Algorithms for Large-Scale Constrained Optimization

这个为期三年的奖项支持正在进行的美法合作 西北大学的豪尔赫·诺塞达尔 法国国立大学和INRIA的Jean Charles吉尔伯特 计算机科学与应用研究所 数学）。 调查人员以前研究的是算法 用于同化的无约束优化 气象问题。 他们打算扩大他们的研究 解决大规模约束优化问题 涉及大量的变量。 目标是 开发更快、更灵活的算法和软件， 目前可用。 研究人员选择了一个重要的非线性问题， 任意大数量的变量-优化的 航天飞机重新进入大气层时的轨迹。 他们将把序列二次规划方法应用于此 问题. 该提案利用互补性 专业知识 美国调查员为这次合作带来了 丰富的大型软件开发经验 规模优化 这是法国人的补充 研究人员在解决最优控制问题方面的专长 以及最近对航天飞机控制的研究。 项目 可能会导致实际应用，但将促进使用 算法和软件，可以在工程和 有大量变量的科学问题。

This three-year award supports ongoing U.S-France collaboration in computer science between Jorge Nocedal of Northwestern University and Jean Charles Gilbert of INRIA (The French National Institute for Research in Computer Science and Applied Mathematics). The investigators worked previously on algorithms for unconstrained optimization as applied to assimilation problems in meteorology. They propose to extend their research to solving large-scale constrained optimization problems involving a large number of variables. The objective is to develop faster and more flexible algorithms and software than is currently available. The investigators selected an important nonlinear problem with an arbitrarily large number of variables - the optimization of the trajectory of a space shuttle as it re-enters the atmosphere. They will apply sequential quadratic programming methods to this problem. The proposal takes advantage of complementary expertise. The U.S. investigator brings to this collaboration extensive experience in the development of software for large scale optimization. This is complemented by the French investigator's expertise in solutions of optimal control problems and recent studies of control of the space shuttle. The project may lead to practical applications, but will advance the use of algorithms and software which may useful in engineering and scientific problems with large numbers of variables.