Optimization in scipy

In optimization we seek the best solution to a problem, often by minimizing some undesirable quantity, or maximizing a desirable quantity, sometimes with constraints that help determine where the best solution is. scipy provides a lot of support for solving a broad range of optimization problems in science and engineering. This booklet will show you many of the options available in scipy including:

1. Minimization/maximization of single and multivariable functions including equality and inequality constraints
2. Linear programming with equality and inequality constraints
3. Mixed Integer programming
4. Global optimization

After reading this booklet you will have a good working knowledge of basic optimization concepts and how to use Python and scipy to solve many of the optimization problems described above. Of course, there are more sophisticated optimization tools available, but they rely on advanced knowledge of Python. This booklet will prepare you for getting started with those libraries.