EE 710 -LARGE SPARSE MATRIX COMPUTATIONS

Session – Spring 2018

Instructor – Prof S. A. Soman

Motivation: This course was supposed to deal with large and sparse matrices, which was not actually covered owing to shortage of classes

Course Content –

*Matrix Norms *Least squares *SVD *Perturbation analysis *Iterative methods

Prerequisite –
The professor teaches from scratch. Hence no prerequisites.

Feedback on the lectures –

The classes were regularly cancelled since the professor was busy. Some extra classes were taken. To teach a particular concept, the professor would pick a related question and give it for the students to solve in the classroom. After everyone has a shot at the question, he would discuss the answer and relevant concepts with explanation.

Feedback on Exams –
The exams were a mixture of announced quizzes, surprise quizzes and quizzes announced just a day before. Most of them were open book exams. This counted to 50% of the weightage of the quiz. The other 50% weightage was given to solving the quiz paper back in your room and submitting it as an assignment within a week of the actual quiz.

The course had an easy endsem and no midsem.

Grading and Difficulty –
Please refer to ASC. There are strong reasons to believe that an absolute grading policy was followed. The exams were on the easier side, and hence it was easy to score.

Attendance : The professor was very lenient on the attendance. But you would not want to miss the surprise quizzes!

Textbooks/References –
Matrix Computations – Golub van Loan Fundamentals of Matrix Computations – David Watkins

Reviewed by Sravan Patchala (sravps7@gmail.com)