AMSC 460 Computational Methods
Spring 2011
Course Description:
Basic computational methods for interpolation, least squares, approximation, numerical quadrature, numerical solution of nonlinear equations, systems of linear equations, and initial value problems for ordinary differential equations. Emphasis on the methods and their computational properties rather on their analytic aspects.
Prerequisite/Corequisite and Credits:
MATH 240 and 241, CMSC 105 or CMSC 106 or CMSC 114 or ENEE 114.
Homework assignment will include exercises using MATLAB;
Familiarity with and accessibility to MATLAB are expected.
Required: Introduction to Scientific Computing (2nd Edition)
by C.F. van Loan [ISBN 0139491570]
Suggested: Numerical Computing with Matlab by Cleve B. Moler
    [online text available]
MATLAB Resources:
Access to MATLAB: Individual students are responsible for gaining the access to MATLAB
Using the univeristy computes that already have MATLAB
Student Edition of MATLAB available from UMD OIT
Users guides
Tutorial at University of Maryland.
Introduction at University of Maryland.
Many other resources are available online.
Demo codes
Basic plotting [Feb 15]
Instructor: Kayo Ide
Email:   ide at
Office at CSCAMM: CSIC 4127
    at AOSC: CSS  3403
Grader: Adrienne Norwood
Email:   anorwood at [contact by email for appointment]
9:30am-10:45am TuTh [CSIC 2118] Class
2:00pm- 3:00pm Tu [CSS 3403] Office hour
3:00pm- 4:00pm W [CSIC 4127] Office hour
11:00am-12:30am, May 9 Mon [CSS 3403] Office hour
March 31, in class Th Mid Term
May 13, 8:00am-10:00am F Final
Outline of the Course:
Topics to be Covered
Introduction, Computer Arithmetic, and Errors
Course Overview
Floating Point Representation
Approximation and Error Analysis
Interpolation and Approximation of Functions
Polynomical Interpolation
Piecewise Polynomial Interpolation
Spline Interpolation
Simple Rules
Adaptive Rules
Matrix Computation
Solution of Linear Equations
Gaussian Elimination
Pivoting and Conditioning
Sparse Systems
Least Squares
Solution of Nonlinear Equations and Optimization
Rootfinding and Minimization of Scalar Functions
Minimization of Multivariate Functions
Solution of Ordinary Differential Equations
One- and Multi-step Methods
Stiff Systems
Grading & Policy:
Grades will be based on: homework (5+) 40%; mid-term 25%; final 35%.
Students are responsible for checking the UMD Honor code (
  No late homework will be accepted without prior arrangement.
  Use of external references should be cited.
  Students may study together and discuss problems and methods of solution with each other to improve understanding in a general way.
  Clear similarities between your work and others will result in a grade reduction for all parties. Flagrant violations will be referred to appropriate university authorities.
Homework Sets:
Homework 1 [Problem Set]
Homework 2 [Problem Set]
Homework 3 [Problem Set]
Homework 4 [Mid-Term OT]
Homework 5 [Problem Set]
Homework 6 [Problem Set]
Matlab Exercises:
Exercise 1 Version 2 [Problem Set | Matlab codes]
Exercise 2 Version 2 [Problem Set]
Exercise 3 [Problem Set]
Exercise 4 [Problem Set]
Final Review
Printing This Page:
Use "selected frame" option in print application or downlad a PDF version (as of Feb 22).
Kayo Ide at UMD AMSC 460 Spring 2011