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lecture 16 notes

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@ -198,3 +198,10 @@ The *nice* case of diagonalization is when you have **orthonormal eigenvectors**
**Further reading:** See links above, and further reading from lecture 13.
## Lecture 16 (Mar 13)
* [Optimization overview](https://docs.google.com/presentation/d/1K8BFfd-_6IWML2zpj_V88GvBjuJiK8EPHqYabDMU3CU/edit?usp=sharing) slides
Broad overview of optimization problems (see slides). The most general formulation is actually quite difficult to solve, so most algorithms (especially the most efficient algorithms) solve various special cases, and it is important to know what the key factors are that distinguish a particular problem. There is also something of an art to the problem formulation itself, e.g. a nondifferentiable minimax problem can be reformulated as a nicer differentiable problem with differentiable constraints.
**Further reading:** There are many textbooks on [nonlinear optimization](http://www.athenasc.com/nonlinbook.html) algorithms of various sorts, including specialized books on [convex optimization](http://web.stanford.edu/~boyd/cvxbook/), [derivative-free optimization](http://bookstore.siam.org/mp08/), etcetera. A useful review of topology-optimization methods can be found in [Sigmund and Maute (2013)](https://link.springer.com/article/10.1007/s00158-013-0978-6).