I'm looking for a good textbook for undergrads that covers some NLP and some IP. The students will be junior and senior Math Sciences majors. They will have seen a semester of LP (simplex method and duality) and basic network models and algorithms, and this is a follow-on optimization course. They have linear algebra and calculus III. Ideally, there would be some decent-sized models to solve. I plan to have them use a computer modeling package such as AMPL or MPL, as well as doing some mathematics. Some topics I'd cover include - Review of basic unconstrained optimization
- Descent methods, Newton's method
- KKT conditions
- Some algorithms for constrained convex optimization
- Branch and bound
- Basic cutting planes and maybe convexification
Other topic suggestions welcome as well.
asked
Matthew Salt... ♦ |

I can't recall seeing NLP and IP in the same book. Maybe you could go the "custom publishing" route?
answered
Paul Rubin ♦♦ |

I have used Bazaraa's 'Nonlinear Programming: Theory and Algorithms' for reference a few times and I found it easy to browse and to understand some basic results.
answered
Thiago Serra |

Of course Bazaraa's 'Nonlinear Programming: Theory and Algorithms' is a fantastic book. But, I can also recommend the book "Engineering Optimization: Theory and Practice" of S.S. Rao. The book covers both NLP and IP but it is suitable for engineering students since it has less mathematical contents (in comparison to Bazaraa's NLP book or the IP book of Wolsey and Nemhauser) and I can say that it is a book on methodology than mathematical bases.
answered
mhdm |