This course covers various mathematical problems introduced in earlier math courses through a numerical computation perspective. There is extensive use of MATLAB for assignments, labs, and on exams.

Digital Computers: binary-decimal conversion, floating point representation, IEEE, overflow/underflow, roundoff and truncation error. Nonlinear Equations: Bisection Method, False-Position Method, Newton-Raphson Method, Secant Method, Fixed-Point Iteration Method. Systems of Linear Equations: determinants, Cramer's Rule, Gaussian Elimination, partial pivoting, LU Decomposition, Jacobi Method, Gauss-Seidel Method, sparse systems, convergence criteria. Systems of Nonlinear Equations: Newton-Raphson Method. Eigenvectors & Eigenvalues: Basic Power Method, Inverse Power Method, Shifted Inverse Power Method, convergence criteria. Interpolation: Weirstrass Theorem, Vandermonde Matrix, Lagrange Polynomial, Newton's Polynomial, linear splines, quadratic splines, cubic splines. Curve-Fitting: l...read more

A very caring but tough professor. He takes the time in class to answer questions, give hints for assignments, and work through examples. He is also accommodating to student needs and responsive outside office hours. His exams and assignments are, however, extremely detailed and comprehensive, making them long and difficult.

Ask questions because he will answer them. Work on the assignments as a team and try to understand each question. Get good with MATLAB fast, as all assignments will require implementing numerical methods using code.