Higher Engineering Mathematics

395

Author: Veerarajan T

ISBN: 9789380381503

Copy Right Year: 2016

Pages:  462

Binding: Soft Cover

Publisher:  Yes Dee Publishing

SKU: 9789380381503 Category:

Description

This book caters to the requirements of Post Graduate students of various Engineering Colleges affiliated to Anna University. This book has simple and lucid presentations with a range of solved examples which enables the students to self-study and understand the topics with ease. The book has a methodical approach towards problem solving and helps the students grasp the topics and solve the exercise problems with confidence. The answers for the exercise problems are given at the end of each chapter.

Additional information

Weight .65 kg
Dimensions 23 × 17 × 2 cm

Table of Content

Chapter 1 LINEAR ALGEBRA AND ADVANCED MATRIX THEORY
1.1 Vector Space
1.2 Inner Product
1.3 Norm
1.4 Generalised Eigenvector
1.5 Canonical Forms
1.5.1 Rational Normal Form or Companion Canonical Form or Natural
Normal Form
1.5.2 Jordan Normal Form or Classical Canonical Form
1.5.3 Smith’s Normal Form
Worked Examples 1(a)
Exercise 1(a)
1.6 QR-Factorisation
1.6.1 Revised Gram-Schmidt Method of Decomposing A
1.6.2 House Holders Method of Decomposing a Symmetric Matrix A
1.7 Least Square Method
1.8 Singular Value Decomposition of an (m × n) Rectangular Matrix A, when
m ≥ n
Worked Examples 1(b)
Exercise 1(b)
Answers
Chapter 2 CALCULUS  OF  VARIATIONS
2.1 Euler-Lagrange Equation for the Extremum of the Functional v[y(x)] between Fixed End Points
2.1.1 Variational Notation
2.1.2 Alternate Method of Deriving Euler’s Equation, using Variational Notation
2.1.3 First Integral of Euler’s Equation, when F is a Function of y
and y_ only, viz., F does contain x Explicitly
2.1.4 Necessary Conditions for the Extremum of a Functional of Two Functions
Between Fixed End Points
2.1.5 Necessary Condition for the Extremum of a Functional Depending on the First and Second Derivatives of the Function y
2.2 Necessary Condition for the Extremum of a Functional Depending on a Function of Two Independent Variables
Worked Examples 2(a)
Exercise 2(a)
2.3 Variational Problems Involving a Conditional Extremum
2.4 Variational Problems with Moving Boundaries/Variables End Points
2.4.1 Natural Boundary Conditions
2.5 Sufficient Conditions for the Extremum of the Functional
2.6 Direct Methods
2.6.1 Ritz Method
2.6.2 Kantorovich Method (or) Semi Direct Method
Worked Examples 2(b)
Exercise 2(b)
Answers
Chapter 3 TENSOR ANALYSIS
3.1 Summation Convention
3.2 Covariant and Contravariant Vectors
3.3 Second Order Tensors
3.3.1 The Kronecker Delta δji
3.3.2 Tensors of Higher Rank (Order)
3.4 Basic Operations of Tensors
3.4.1 Addition and Subtraction
3.4.2 Outer Multiplication
3.4.3 Contraction
3.4.4 Inner Multiplication
3.4.5 Quotient Law
3.5 Symmetric and Skew-Symmetric Tensors
3.5.1 Metric Tensor or Fundamental Tensor
3.5.2 Conjugate or Reciprocal Tensor of gij
Worked Examples 3(a)
Exercise 3(a)
3.6 Christoffel’s Symbols
3.6.1 Derivatives of the Fundamental Tensors
3.6.2 Transformation Law for Christoffel Symbol of the I Kind
3.6.3 Transformation Law for Christoffel Symbol of the II Kind
3.7 Covariant Differentiation
3.7.1 The Covariant Derivative Ai, j is a Covariant Tensor of Rank 2
3.7.2 The Covariant Derivative Ai, j is a Mixed Tensor of Rank 2
3.8 Gradient, Divergence and Curl in Tensor Notation
Worked Examples 3(b)
Exercise 3(b)
Answers
Chapter 4 LAPLACE TRANSFORMS AND GENERALISED
FOURIER SERIES
4.1 Laplace Transforms of the Bessel’s Functions J0(x) and J1(x)
4.2 Laplace Transform of Error Function
4.3 Complex Inverse Formula
Worked Examples 4(a)
Exercise 4(a)
4.4 Generalised Fourier Series
4.4.1 Orthogonal Functions
4.5 Sturm−Liouville Systems
Worked Examples 4(b)
Exercise 4(b)
Answers
Chapter 5 ESTIMATION THEORY
5.1 Interval Estimation
5.2 Point Estimation
5.2.1 Unbiased Estimator
5.2.2 Consistent Estimator
5.2.3 Efficient Estimator
5.2.4 Most Efficient Estimator
5.2.5 Sufficient Estimator
5.2.6 Neymann’s Factorisation Criterion for a Sufficient Estimator
Worked Examples 5(a)
Exercise 5(a)
5.3 Methods of Finding Estimators
5.3.1 Method of Maximum Likelihood
5.3.2 Method of Moments
Worked Examples 5(b)
Exercise 5(b)
Answers
Chapter 6 MULTIPLE AND PARTIAL CORRELATIONS
6.1 A Note on Yule’s Subscript Notation
6.2 Plane of Regression
6.3 Properties of Residuals
6.4 Coefficient of Multiple Correlation
6.4.1 Multiple Correlation Coefficient in terms of Simple Correlation Coefficients
6.5 Partial Correlation Coefficient in Terms of Simple Correlation Coefficients
Worked Examples 6
Exercise 6
Answers
Chapter 7 MULTIVARIATE ANALYSIS
7.1 Mean Vectors and Covariance Matrices
7.2 Partitioning the Covariance Matrix
7.3 Mean Vector and Covariance Matrix for Linear Combination of Random Variables
7.4 Multivariate Normal (MVN) Distribution
7.4.1 Importance of the MVN Distribution
7.4.2 Properties of a MVN Distribution
7.5 Principal Components
7.5.1 Population Principal Components
7.5.2 Principal Components got from Standardised Variables
Worked Examples 7
Exercise 7
Answers
Chapter 8 LINEAR PROGRAMMING
8.1 Linear Programming Problem (L.P.P)
8.2 Formulation of a Linear Programming Problem
8.2.1 Solution of a Linear Programming Problem
8.2.2 Step by Step Procedure of the Graphical Method
8.2.3 Important Special Cases
Worked Examples 8(a)
Exercise 8(a)
8.3 Simplex Method
8.3.1 Some Definitions and Notations
8.3.2 Slack Variable
8.3.3 Surplus Variable
8.3.4 Initial Basic Feasible Solution
8.3.5 Computational Procedure of the Simplex Method
Worked Examples 8(b)
Exercise 8(b)
8.4 Transportation Problem
8.4.1 Initial B.F.S. by North-West Corner Rule
8.4.2 Initial B.F.S. by the Lowest Cost Entry Method or Matrix Minima Method
8.4.3 Initial B.F.S. by Vogel’s Approximation Method (VAM) or Unit Cost Penalty Method
8.4.4 Step II Optimality Test – Computational Procedure
8.4.5 Step III
8.4.6 Unbalanced Transportation Problem
8.4.7 Alternative Optimal Solution of a Transportation Problem
8.4.8 Maximisation Case in Transportation Problem
Worked Examples 8(c)
Exercise 8(c)
8.5 Assignment Problem
8.5.1 Assignment Algorithm (Hungarian Method or Flood’s Technique)
Worked Examples 8(d)
Exercise 8(d)
Answers
Chapter 9 DYNAMIC PROGRAMMING
9.1 Bellman’s Principle of Optimality
Worked Examples 9
9.2 Solution of Linear Programming Problem as a Dynamic Programming Problem
Exercise 9
Answers

About The Author

T. Veerarajan is Dean (Retd.), Department of Mathematics, Velammal College of Engineering and Technology, Madurai, Tamil Nadu. A Gold Medalist from Madras University, he has had a brilliant academic career all through. He has 50 years of teaching experience at undergraduate and postgraduate levels in various established engineering colleges in Tamil Nadu including Anna University, Chennai.

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