Institute of Engineering & Management
Course: M(CS) 401 Numerical Methods PROGRAMME:
COMPUTER SCIENCE & ENGINEERING
COURSE: NUMERICAL METHODS COURSE CODE: REGULATION:
M ( CS ) 401
DEGREE: BTECH SEMESTER: IV
CREDITS: 2
COURSE TYPE: THEORY
2015
COURSE AREA/DOMAIN: DESIGNING OF COMPUTERS
CONTACT HOURS: 2L/WEEK + 1T/WEEK
CORRESPONDING LAB COURSE CODE (IF ANY): M(CS)491
LAB COURSE NAME: NUMERICAL
LAB
METHODS
Course pre-requisites CODE
COURSE NAME
DESCRIPTION
SEM
Course Objectives The primary goal is to provide engineering majors with a basic knowledge of numerical methods including: root-finding, elementary numerical linear algebra, integration, interpolation, solving systems of linear equations, curve fitting, and numerical solution to ordinary differential equations. ‘C’ language and SCILAB is the software environment used for implementation and application of these numerical methods. The numerical techniques learned in this course enable students to work with mathematical models of technology and systems.
Course Outcomes 1. 2. 3. 4.
Students would be able to assess the approximation techniques to formulate and apply appropriate strategy to solve real world problems. Be aware of the use of numerical methods in modern scientific computing. Be familiar with finite precision computation. Be familiar with numerical solution of integration, linear equations, ordinary differential equations, interpolations.
Programme Outcomes addressed in this course (a) An ability to apply knowledge of mathematics, science, and engineering. (b) An ability to design and conduct experiments, as well as to analyze and interpret data. (c) An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability (d) An ability to function on multidisciplinary teams
Department of CSE
Page 1
Institute of Engineering & Management Syllabus MODULE
I
DETAILS
LECTURE
Approximation in numerical computation: Truncation and rounding errors, Fixed and floating-point arithmetic, Propagation of errors. 4
II
Interpolation: Newton forward/backward interpolation, Lagrange’s and Newton’s divided difference Interpolation.
5
III
Numerical integration: Trapezoidal rule, Simpson’s 1/3 rule, Expression for corresponding error terms. 3
IV
V
VI
Numerical solution of a system of linear equations: Gauss elimination method, Matrix inversion, LU Factorization method, Gauss-Seidel iterative method.
6
Numerical solution of Algebraic equation: Bisection method, Regula-Falsi method, Newton-Raphson method.
4
Numerical solution of ordinary differential equation: Euler’s method, Runge-Kutta methods, Predictor-Corrector methods and Finite Difference method.
6
TOTAL HOURS
28
Gaps in the syllabus - to meet industry/profession requirements S.NO.
1.
DESCRIPTION
Weddle’s rule and Simpson’s 3/8 rule
PROPOSED ACTIONS
Extra classes
PO MAPPING a
Topics beyond syllabus/advanced topics S.NO.
Department of CSE
DESCRIPTION
HOURS
Page 2
Institute of Engineering & Management
1
Numerical integration – Weddle’s rule, Simpson’s 3/8 rule
3
Delivery/Instructional Methodologies S.NO.
DESCRIPTION
1
Chalk and Talk
2
Study Material
Assessment Methodologies S.NO.
DESCRIPTION
TYPE
2
Student Assignment . Tests
3
University Examination
Direct
4
Student Feedback
Indirect
1
Direct Direct
Course Plan S. NO. 1 2 3 4 5 6 7 8 9
Day Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9
Module I I I I II II II II II
10
Day 10
III
11 12
Day 11 Day 12
III III
Department of CSE
Topic Introduction Different types of Errors; Different types of Errors; Different types of Errors; Interpolation technique – Equal and unequal intervals Newton’s forward interpolation; Newton’s Backward interpolation; Unequal interpolation - Newton’s divided difference; Lagrange’s Interpolation; Numerical integration – Newton cotes quadrature formula; Trapezoidal rule; Trapezoidal rule; Simpson’s 1/3 rule; Weddle rule; Simpson’s 3/8 rule; Page 3
Institute of Engineering & Management
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Day 13 Day 14 Day 15 Day 16 Day 17 Day 18 Day 19 Day 20 Day 21 Day 22 Day 23 Day 24 Day 25 Day 26 Day 27 Day 28
III V V V IV IV IV IV VI VI VI VI VI VI VI VI
Error terms of Trapezoidal rule and Simpson’s 1/3 rule; Error terms of Trapezoidal rule and Simpson’s 1/3 rule; Bisection method; Rgula falsi method; Newton Raphson Method; System of linear equations: Matrix inversion; Gauss elimination method; LU factorization; Gauss-seidal method. ODE: Euler’s method Modified Euler’s method Runge-kutta order 2 Runge-kutta order 4 Predictor Corrector method Predictor Corrector method Finite difference method Finite difference method
Assignment Set I S. NO.
Question
1
State sufficient condition for convergence of Gauss-Seidel method.
2
Assesses CO
Find the solutions of the following system of equations by L-U factorization
CO.1 CO.1
method 2x-3y+10z=3 -x+4y+2z=20 5x+2y+z=-12
3
Solve the following system of equations by Matrix inversion method. 3x+y+2z = 3
CO.1
2x-3y-z = -3 x+2y+z = 4
4
Find the smallest positive root of the equation f(x) = x3-3x-5=0 for five iterations.
Department of CSE
CO.1
Page 4
Institute of Engineering & Management
Department of CSE
Page 5