Unformatted text preview: ERG 2018
Advanced Engineering Mathematics Lecture Notes Dr. K.P. Lam Sept, 2008 1 Major topics
Calculus Calculus of multiple variables Linear Linear algebra and vector space Linear Linear transformation, matrix decomposition, eigenanalysis eigen 2 Introduction
Motivations Motivations
• To apply mathematics for practical applications in engineering, economics, finance, and others • To consider practical cases and examples where the major topics can be applied 3 Introduction example 1 Physical 1system modeling
Laplace Laplace equation for the temperature inside a rectangular container
• Partial derivatives and ordinary derivatives, gradient and curvature, first and second derivatives • Laplace operator on temperature • Formation of linear equation set • Other domains (electromagnetic field, biomedicine, weather forecasting)
4 Introduction example 2 – Financial modeling and forecasting
TimeTimeseries modeling for stock prices
• Stock price today may have a linear relationship with the prices yesterday and the day before • Formation of a linear equation model • Using historical data to obtain the model • Using the model for forecasting 5 Linear equations and matrices
Two Two linear equations
• Using the row and column indexes (to prepare for generalization) • Geometrical interpretations in a 2D plane 2(intersection, parallel lines, overlap) • Matrix formulation ( Ax = y ) Generalization nGeneralization to ndimensional case 6 Solving linear equations
Cramer’s Cramer’s rule
• 2dimensional and 3dimensional cases 3but becomes tedious for high dimensions • Matrix determinants Gauss Gauss elimination
• Good for a general ndimensional system n• Twostep procedure: forward elimination Twoand backward substitution
7 Backward substitution
Consider Consider special cases for A: L and U as lower and upper triangular matrices Solving Solving Ux=y is much simpler (why?)
• 2dimensional: x2 easily obtained, then x1 • 3dimensional: x3 easily obtained, then x2 and x1 • ndimensional?
8 Forward elimination
How How should we reduce a general Ax=y to Ux = z? Use Use elementary row operations on the augmented matrix [A  y] and change it to [U  z] in a columnwise manner Example: 2Example: 2dimensional case How How can we generalize to a procedure for general n dimensions?
9 GaussGaussJordan and matrix inverse
Finding Finding x by the reduction of [A y] to [I  z] using elementary row operations – GaussGaussJordan
• 3dimensional example Finding Finding x for Ax=y using x=A1y. Can we find matrix inverse using similar GaussGaussJordan procedure?
10 Vectors
Basic Basic vector operations Norm, Norm, dot product, and angle for nnspace Orthogonality Orthogonality Schwarz Schwarz inequality
• The relationship between u.v and the u.v norms • Triangle inequality
11 11 Vector space
Definition Definition of a vector space. What are the main properties? Subspace Subspace and span Linear Linear dependence (LD) of a set of vectors
• Linear combination of vectors 12 ...
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 Spring '10
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 Linear Algebra, space Linear Linear, variables Linear Linear

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