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Title: 9700 South Cass Avenue
Title: 9700 South Cass Avenue


Research Question: How can optimization algorithms be designed and implemented to effectively solve large-scale problems on parallel architectures?
Research Question: How can performance profiles be used to benchmark and compare optimization software?


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.
Methodology: The study proposes the use of performance profiles - distribution functions for a performance metric - as a tool for benchmarking and comparing optimization software. The authors demonstrate that performance profiles combine the best features of other tools for performance evaluation.


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.
Results: The paper presents three case studies: optimal control and parameter estimation problems, the Full COPS, and linear programming. Each case study showcases the effectiveness of performance profiles in providing insight into the software's performance.


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.
Implications: The use of performance profiles offers a comprehensive and accessible way to evaluate and compare optimization software. It allows for a better understanding of the software's performance trends and provides a more accurate comparison between different solvers.


Link to Article: https://arxiv.org/abs/0101018v1
Conclusion: In conclusion, performance profiles are a valuable tool for benchmarking and comparing optimization software. They combine the best features of other tools for performance evaluation and provide a more comprehensive and accurate comparison of software performance.
 
Link to Article: https://arxiv.org/abs/0102001v1
Authors:  
Authors:  
arXiv ID: 0101018v1
arXiv ID: 0102001v1


[[Category:Computer Science]]
[[Category:Computer Science]]
[[Category:Performance]]
[[Category:Software]]
[[Category:Profiles]]
[[Category:Optimization]]
[[Category:Optimization]]
[[Category:Parallel]]
[[Category:Compare]]
[[Category:Gpcg]]
[[Category:Algorithm]]
[[Category:Study]]

Revision as of 02:00, 24 December 2023

Title: 9700 South Cass Avenue

Research Question: How can performance profiles be used to benchmark and compare optimization software?

Methodology: The study proposes the use of performance profiles - distribution functions for a performance metric - as a tool for benchmarking and comparing optimization software. The authors demonstrate that performance profiles combine the best features of other tools for performance evaluation.

Results: The paper presents three case studies: optimal control and parameter estimation problems, the Full COPS, and linear programming. Each case study showcases the effectiveness of performance profiles in providing insight into the software's performance.

Implications: The use of performance profiles offers a comprehensive and accessible way to evaluate and compare optimization software. It allows for a better understanding of the software's performance trends and provides a more accurate comparison between different solvers.

Conclusion: In conclusion, performance profiles are a valuable tool for benchmarking and comparing optimization software. They combine the best features of other tools for performance evaluation and provide a more comprehensive and accurate comparison of software performance.

Link to Article: https://arxiv.org/abs/0102001v1 Authors: arXiv ID: 0102001v1

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