# Tutorials on Advanced Optimization Methods

arXiv: https://arxiv.org/abs/2007.13545 · HTML: https://mlsanity.com/paper/2007.13545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

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

- [Collective Certified Robustness against Graph Injection Attacks](https://mlsanity.com/paper/2403.01423/) (2024)
- [Data-driven Power Flow Linearization: Theory](https://mlsanity.com/paper/2407.02501/) (2024)

## Abstract (arXiv)

This material provides thorough tutorials on some optimization techniques frequently used in various engineering disciplines, including convex optimization, linearization techniques and mixed-integer linear programming, robust optimization, and equilibrium/game problems. It discusses how to reformulate a difficult (non-convex, multi-agent, min-max) problem to a solver-compatible form (semidefinite program, mixed-integer linear program) via convexification, linearization, and decomposition, so the original problem can be reliably solved by commercial/open-source software. Fundamental algorithms are not the main focus. This material is a good reference for self-learners who have basic knowledge in linear algebra and linear programming. It is one of the main references for an optimization course taught at Tsinghua University.