This class will cover several topics, including graph clustering, graph partitioning, solving linear systems of equations, page rank algorithm, and large-scale nonlinear optimization. As much as possible, numerical methods will be presented in the context of real-world applications.
Numerical computing is an interconnected combination of computer science and mathematics in which we develop and analyze algorithms for solving important problems in science, engineering, medicine, and business -- for example, simulating an earthquake, choosing a stock portfolio, or detecting cancer tumors in medical images. The students will learn principles and practices of basic numerical computations by tackling six to eight mini-projects. This is a key aspect of scientific computation.
A goal of the course is that students will learn principles and practices of basic numerical methods to enable scientific numerical simulations. This goal will be achieved within six to eight mini-projects with a focus on numerical computing.
40% of the grade is determined by mandatory graded projects and 60% is determined by a final written or oral exam during the official examination period.