9700 South Cass Avenue: Difference between revisions
Created page with "Title: 9700 South Cass Avenue Research Question: How can automatic differentiation tools be used to improve the efficiency and accuracy of optimization software? Methodology: The study uses the NEOS Server, a problem-solving environment that integrates automatic differentiation tools with state-of-the-art optimization solvers. It discusses the computation of the gradient and Hessian matrix for partially separable functions, highlighting the benefits of using automatic..." |
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Title: 9700 South Cass Avenue | Title: 9700 South Cass Avenue | ||
Research Question: How can | Research Question: How can optimization algorithms be designed and implemented to effectively solve large-scale problems on parallel architectures? | ||
Methodology: The study uses the | Methodology: The study uses the Toolkit for Advanced Optimization (TAO), a component-based optimization software designed for large-scale applications. It focuses on the Gradient Projection Conjugate Gradient (GPCG) algorithm, an optimization method for solving bound-constrained quadratic programming problems. The GPCG algorithm was implemented on a parallel architecture using object-oriented techniques and the PETSc library for linear algebra support. | ||
Results: The | Results: The implementation of GPCG on a parallel architecture showed promising results. The algorithm's performance and scalability were analyzed, revealing that the scalability is limited by the sizes of the matrices involved in the optimization process. The study found that the GPCG algorithm is a prime candidate for a case study on the performance and scalability of optimization algorithms in parallel architectures. | ||
Implications: The | Implications: The results suggest that the GPCG algorithm, when implemented on a parallel architecture, can be an effective tool for solving large-scale optimization problems. The study also highlights the importance of object-oriented techniques and linear algebra support in designing and implementing optimization algorithms for parallel architectures. | ||
Link to Article: https://arxiv.org/abs/ | Link to Article: https://arxiv.org/abs/0101018v1 | ||
Authors: | Authors: | ||
arXiv ID: | arXiv ID: 0101018v1 | ||
[[Category:Computer Science]] | [[Category:Computer Science]] | ||
[[Category:Optimization]] | |||
[[Category:Parallel]] | |||
[[Category:Gpcg]] | |||
[[Category:Algorithm]] | |||
[[Category:Study]] | |||
Revision as of 01:55, 24 December 2023
Title: 9700 South Cass Avenue
Research Question: How can optimization algorithms be designed and implemented to effectively solve large-scale problems on parallel architectures?
Methodology: The study uses the Toolkit for Advanced Optimization (TAO), a component-based optimization software designed for large-scale applications. It focuses on the Gradient Projection Conjugate Gradient (GPCG) algorithm, an optimization method for solving bound-constrained quadratic programming problems. The GPCG algorithm was implemented on a parallel architecture using object-oriented techniques and the PETSc library for linear algebra support.
Results: The implementation of GPCG on a parallel architecture showed promising results. The algorithm's performance and scalability were analyzed, revealing that the scalability is limited by the sizes of the matrices involved in the optimization process. The study found that the GPCG algorithm is a prime candidate for a case study on the performance and scalability of optimization algorithms in parallel architectures.
Implications: The results suggest that the GPCG algorithm, when implemented on a parallel architecture, can be an effective tool for solving large-scale optimization problems. The study also highlights the importance of object-oriented techniques and linear algebra support in designing and implementing optimization algorithms for parallel architectures.
Link to Article: https://arxiv.org/abs/0101018v1 Authors: arXiv ID: 0101018v1