Skills in computational and applied mathematics prove invaluable to every quantitative discipline. Many students would benefit from a richer foundation in applied mathematics.

A **minor in Computational and Applied mathematics at Rice University** will provide you a core of knowledge in the discipline while allowing you sufficient flexibility to tailor your curriculum to complement your major interests. This program of study particularly appeals to students majoring in engineering, natural sciences, and economics.

For an overview of the Minor in Computational and Applied Mathematics, learning outcomes, requirements and more, visit General Announcements.

## Courses

All students will take courses in basic mathematical modeling and computing (CMOR 220) and linear algebra (CMOR 302 or CMOR 303), along with a course in either partial differential equations (CMOR 304) or optimization/operations research (CMOR 360).

Students then will select three more courses that could, for example, focus on computational science and engineering, differential equations or optimization; see the samples below.

## Requirements

To obtain a CAAM minor, students must complete at least 18 credit hours in the CAAM department, including:

- CMOR 220 Introduction to Engineering Computation
- CMOR 303 Matrix Analysis for Data Science
**(or)**CMOR 302 Matrix Analysis- CMOR 304 Differential Equations in Science and Engineering
**(or)**CMOR 360 Introduction to Operations Research and Optimization- Three additional 3-credit CMOR courses, at least two of which must be at the 400 level or above

## Sample Programs of Study

These programs are only suggestions. Students are encouraged to consult with a CMOR undergraduate advisor to tailor a program that best meets their interests.

### Computational Science and Engineering

- CMOR 220 Introduction to Engineering Computation
- CMOR 303 Matrix Analysis for Data Science
**(or)**CMOR 302 Matrix Analysis- CMOR 304 Differential Equations in Science and Engineering
- CMOR 422 Numerical Analysis I
- CMOR 520 Computational Science
- CMOR 521 High Performance Computing

### Differential Equations

- CMOR 220 Introduction to Engineering Computation
- CMOR 303 Matrix Analysis for Data Science
**(or)**CMOR 302 Matrix Analysis- CMOR 304 Differential Equations in Science and Engineering
- CMOR 422 Numerical Analysis I
- CMOR 415 Theoretical Neuroscience
- or CMOR 405 Partial Differential Equations I
- CMOR 523 Numerical Methods for Partial Differential Equations

### Optimization

- CMOR 220 Introduction to Engineering Computation
- CMOR 303 Matrix Analysis for Data Science
**(or)**CMOR 302 Matrix Analysis- CMOR 360 Introduction to Operations Research and Optimization
- CMOR 430 Iterative Methods for Systems of Equations and Unconstrained Optimization
- CMOR 441 Linear and Integer Programming
- CMOR 442 Large-Scale Optimization
- CMOR 504 Graph Theory
- CMOR 543 Combinatorial Optimization