Empowering Computational Science and Engineering via Automatic Differentiation

以下是通过这项工作将解决的具体任务的修订摘要：开发雅可比和Hessian的自动稀疏检测技术。在自动微分工具ADOL-C中实现该技术。进一步开发着色软件包COLPACK并使用新的双着色算法对其进行增强。 将自动微分法应用于化工色谱分离技术中的一类问题。开发COLPACK和ADOL-C软件包之间的接口，使其更广泛地适用于优化代码。 开发伴随计算中有向无环图反转问题的有效算法。通过开发研究生课程，组织小型课程和会议教程，以及撰写论文和书籍章节，为组合科学计算和自动微分的教育和培训做出贡献。 PI将尽一切努力满足修订项目的工作范围和水平。

The following is a revised summary of the specific tasks that will be addressedthrough this effort:Develop automatic sparsity detection techniques for Jacobians and Hessians.Implement the techniques in the Automatic Differentiation tool ADOL-C.Further develop the coloring software package COLPACK and enhance it withnew bicoloring algorithms. Apply Automatic Differentiation to a class of problems in chromatographic separation techniques in chemical engineering. Develop interfaces between the software packages COLPACK and ADOL-C, to enable itswider applicability to optimization codes. Develop efficient algorithms for the directed acyclic graph reversal problem in adjoint computation.Contribute to education and training in combinatorial scientific computing and Automatic Differentiation by developing a graduate level course, organizing mini courses and tutorials in conferences, and by writing expository articles and book chapters. The PIs will make every attempt to meet the scope and level of effort of the revised project.