# Sensitivity analysis for parametric nonlinear programming: A tutorial

arXiv: https://arxiv.org/abs/2504.15851 · HTML: https://mlsanity.com/paper/2504.15851/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [Sobolev Training of End-to-End Optimization Proxies](https://mlsanity.com/paper/2505.11342/) (2025)
- [dOPT: Differentiating Conic Optimization via Geometric Reduction](https://mlsanity.com/paper/2609.33828/) (2026)

## Abstract (arXiv)

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.