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Direct and iterative solvers of dense and sparse linear systems of equations, numerical schemes for handling symmetric algebraic eigenvalue problems, and the singular-value decomposition and its applications in linear least squares problems. Typically offered Spring. Obviously, this course is being held in the fall, not the spring. We will mainly focus on dense and sparse linear systems.

Install Anaconda Python 3. Install MiniConda Python 3. I think that Python is the easiest to get started.

In class we talked about how modern computer graphics algorithms make heavy use of fast numerical linear algebra. Your homework will give you plenty of opportunity to review the fundamentals of linear algebra. For this quiz, we'll use a popular package called Eigen. Eigen gives you access to lots of different linear solvers; you'll just use the most basic one to solve a little linear system. One reason Eigen has become so popular is that it's dead simple to use: the library is a "header only" library, which in practice means you don't have to install or link anythingyou just download a bunch of source code files, and include them in your project.

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