CS 450

CS 450 - Numerical Analysis

Spring 2021

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Numerical AnalysisCS450B331427ONL30930 - 1045 M W    Edgar Solomonik
Numerical AnalysisCS450B431430ONL40930 - 1045 M W    Edgar Solomonik
Numerical AnalysisCSE401B331432ONL30930 - 1045 M W    Edgar Solomonik
Numerical AnalysisCSE401B431434ONL40930 - 1045 M W    Edgar Solomonik
Numerical AnalysisECE491B331436ONL30930 - 1045 M W    Edgar Solomonik
Numerical AnalysisECE491B431438ONL40930 - 1045 M W    Edgar Solomonik
Numerical AnalysisMATH450B331440ONL30930 - 1045 M W    Edgar Solomonik
Numerical AnalysisMATH450B431443ONL40930 - 1045 M W    Edgar Solomonik

Official Description

Linear system solvers, optimization techniques, interpolation and approximation of functions, solving systems of nonlinear equations, eigenvalue problems, least squares, and quadrature; numerical handling of ordinary and partial differential equations. Course Information: Same as CSE 401, ECE 491, and MATH 450. 3 undergraduate hours. 3 or 4 graduate hours. Credit is not given for both CS 450 and CS 457. Prerequisite: CS 101 or CS 125; CS 357 or MATH 415; MATH 285.

Course Director

Text(s)

Scientific Computing : An Introductory Survey, 2nd E, by Michael T. Heath

Learning Goals

Analyze the conditioning of common numerical problems such as solving a linear system, finding eigenvalues, numerical differentiation and integration, etc. (1)

Calculate numerical approximations to solutions to linear and nonlinear systems, eigenvalues/eigenvectors, optimization problems, integrals, derivatives, and solutions to differential equations.(6)

Compare the accuracy and cost of different numerical methods for solving a numerical problem. (1)(6)

Estimate the accuracy and efficiency of numerical approximations.(6)

Write code to solve numerical problems.(2)(6)

Design and carry out numerical experiments to test various numerical methods. (2)(6)

Topic List

Approximations, Error Analysis, and Floating-Point Arithmetic
Systems of Linear Equations
Linear Least Squares
Eigenvalue Problems
Nonlinear Equations
Optimization
Interpolation
Numerical Integration and Differentiation
Initial Value Problems for ODEs
Boundary Value Problems for ODEs
Partial Differential Equations
Fast Fourier Transform

Required, Elective, or Selected Elective

Selected Elective.

Last updated

2/18/2019by Luke Olson