A First Course in Applied Mathematics
Samenvatting
Explore real–world applications of selected mathematical theory, concepts, and methods
Exploring related methods that can be utilized in various fields of practice from science and engineering to business, A First Course in Applied Mathematics details how applied mathematics involves predictions, interpretations, analysis, and mathematical modeling to solve real–world problems.
Written at a level that is accessible to readers from a wide range of scientific and engineering fields, the book masterfully blends standard topics with modern areas of application and provides the needed foundation for transitioning to more advanced subjects. The author utilizes MATLAB® to showcase the presented theory and illustrate interesting real–world applications to Google′s web page ranking algorithm, image compression, cryptography, chaos, and waste management systems. Additional topics covered include:
Linear algebra
Ranking web pages
Matrix factorizations
Least squares
Image compression
Ordinary differential equations
Dynamical systems
Mathematical models
Throughout the book, theoretical and applications–oriented problems and exercises allow readers to test their comprehension of the presented material. An accompanying website features related MATLAB® code and additional resources.
A First Course in Applied Mathematics is an ideal book for mathematics, computer science, and engineering courses at the upper–undergraduate level. The book also serves as a valuable reference for practitioners working with mathematical modeling, computational methods, and the applications of mathematics in their everyday work.
Specificaties
Inhoudsopgave
<p>1. Basics of Linear Algebra 1</p>
<p>1.1 Notation and Terminology 1</p>
<p>1.2 Vector and Matrix Norms 4</p>
<p>1.3 Dot Product and Orthogonality 8</p>
<p>1.4 Special Matrices 9</p>
<p>1.5 Vector Spaces 21</p>
<p>1.6 Linear Independence and Basis 24</p>
<p>1.7 Orthogonalization and Direct Sums 30</p>
<p>1.8 Column Space, Row Space and Null Space 34</p>
<p>1.9 Orthogonal Projections 43</p>
<p>1.10 Eigenvalues and Eigenvectors 47</p>
<p>1.11 Similarity 56</p>
<p>1.12 Bezier Curves Postscripts Fonts 59</p>
<p>1.13 Final Remarks and Further Reading 68</p>
<p>2. Ranking Web Pages 79</p>
<p>2.1 The Power Method 80</p>
<p>2.2 Stochastic, Irreducible and Primitive Matrices 84</p>
<p>2.3 Google s PageRank Algorithm 92</p>
<p>2.4 Alternatives to Power Method 106</p>
<p>2.5 Final Remarks and Further Reading 120</p>
<p>3. Matrix Factorizations 131</p>
<p>3.1 LU Factorization 132</p>
<p>3.2 QR Factorization 142</p>
<p>3.3 Singular Value Decomposition (SVD) 155</p>
<p>3.4 Schur Factorization 166</p>
<p>3.5 Information Retrieval 186</p>
<p>3.6 Partition of Simple Substitution Cryptograms 194</p>
<p>3.7 Final Remarks and Further Reading 203</p>
<p>4. Least Squares 215</p>
<p>4.1 Projections and Normal Equations 215</p>
<p>4.2 Least Squares and QR Factorization 224</p>
<p>4.3 Lagrange Multipliers 228</p>
<p>4.4 Final Remarks and Further Reading 231</p>
<p>5. Image Compression 235</p>
<p>5.1 Compressing with Discrete Cosine Transform 236</p>
<p>5.2 Huffman Coding 260</p>
<p>5.3 Compression with SVD 267</p>
<p>5.4 Final Remarks and Further Reading 271</p>
<p>6. Ordinary Differential Equations 277</p>
<p>6.1 One–Dimensional Differential Equations 278</p>
<p>6.2 Linear Systems of Differential Equations 307</p>
<p>6.3 Solutions via Eigenvalues and Eigenvectors 308</p>
<p>6.4 Fundamentals Matrix Solution 312</p>
<p>6.5 Final Remarks and Further Reading 316</p>
<p>7. Dynamical Systems 325</p>
<p>7.1 Linear Dynamical Systems 326</p>
<p>7.2 Nonlinear Dynamical Systems 340</p>
<p>7.3 Predator–Prey Models with Harvesting 374</p>
<p>7.4 Final Remarks and Further Reading 385</p>
<p>8. Mathematical Models 395</p>
<p>8.1 Optimization of a Waste Management System 396</p>
<p>8.2 Grouping Problem in Networks 404</p>
<p>8.3 American Cutaneous Leishmaniasis 410</p>
<p>8.4 Variable Population Interactions 420</p>
<p>References 431</p>
<p>Index 435 </p>