Sensitivity analysis for parametric nonlinear programming: A tutorial
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910916506812416 |
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| author | Pacaud, François |
| author_facet | Pacaud, François |
| contents | This tutorial provides an overview of the current state-of-the-art in the sensitivity analysis for nonlinear programming. Building upon the fundamental work of Fiacco, it derives the sensitivity of primal-dual solutions for regular nonlinear programs and explores the extent to which Fiacco's framework can be extended to degenerate nonlinear programs with non-unique dual solutions. The survey ends with a discussion on how to adapt the sensitivity analysis for conic programs and approximate solutions obtained from interior-point algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensitivity analysis for parametric nonlinear programming: A tutorial Pacaud, François Optimization and Control This tutorial provides an overview of the current state-of-the-art in the sensitivity analysis for nonlinear programming. Building upon the fundamental work of Fiacco, it derives the sensitivity of primal-dual solutions for regular nonlinear programs and explores the extent to which Fiacco's framework can be extended to degenerate nonlinear programs with non-unique dual solutions. The survey ends with a discussion on how to adapt the sensitivity analysis for conic programs and approximate solutions obtained from interior-point algorithms. |
| title | Sensitivity analysis for parametric nonlinear programming: A tutorial |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2504.15851 |