This book encompasses the full range of computational science and engineering from modelling to solution, whether analytic or numerical.

1. Applied Linear Algebra: 1.1 Four special matrices; 1.2 Differences, derivatives, and boundary conditions; 1.3 Elimination leads to K = LDL^T; 1.4 Inverses and delta functions; 1.5 Eigenvalues and eigenvectors; 1.6 Positive definite matrices; 1.7 Numerical linear algebra: LU, QR, SVD; 1.8 Best basis from the SVD; 2. A Framework for Applied Mathematics: 2.1 Equilibrium and the stiffness matrix; 2.2 Oscillation by Newton's law; 2.3 Least squares for rectangular matrices; 2.4 Graph models and Kirchhoff's laws; 2.5 Networks and transfer functions; 2.6 Nonlinear problems; 2.7 Structures in equilibrium; 2.8 Covariances and recursive least squares; 2.9 Graph cuts and gene clustering; 3. Boundary Value Problems: 3.1 Differential equations of equilibrium; 3.2 Cubic splines and fourth order equations; 3.3 Gradient and divergence; 3.4 Laplace's equation; 3.5 Finite differences and fast Poisson solvers; 3.6 The finite element method; 3.7 Elasticity and solid mechanics; 4. Fourier Series and Integrals: 4.1 Fourier series for periodic functions; 4.2 Chebyshev, Legendre, and Bessel; 4.3 The discrete Fourier transform and the FFT; 4.4 Convolution and signal processing; 4.5 Fourier integrals; 4.6 Deconvolution and integral equations; 4.7 Wavelets and signal processing; 5. Analytic Functions: 5.1 Taylor series and complex integration; 5.2 Famous functions and great theorems; 5.3 The Laplace transform and z-transform; 5.4 Spectral methods of exponential accuracy; 6. Initial Value Problems: 6.1 Introduction; 6.2 Finite difference methods for ODEs; 6.3 Accuracy and stability for u_t = c u_x; 6.4 The wave equation and staggered leapfrog; 6.5 Diffusion, convection, and finance; 6.6 Nonlinear flow and conservation laws; 6.7 Fluid mechanics and Navier-Stokes; 6.8 Level sets and fast marching; 7. Solving Large Systems: 7.1 Elimination with reordering; 7.2 Iterative methods; 7.3 Multigrid methods; 7.4 Conjugate gradients and Krylov subspaces; 8. Optimization and Minimum Principles: 8.1 Two fundamental examples; 8.2 Regularized least squares; 8.3 Calculus of variations; 8.4 Errors in projections and eigenvalues; 8.5 The Saddle Point Stokes problem; 8.6 Linear programming and duality; 8.7 Adjoint methods in design.