Course Objective: Introduction to numerical algorithms and basics of optimisation. Modelling problems as numerical problems.
Pre-requisite: Calculus, Linear Algebra, Data Structures.
Coverage: Number representation, fundamentals of error analysis, conditioning, stability, polynomials and root finding, interpolation, singular value decomposition and its applications, QR factorisation, condition number, least squares and regression and their applications, Gaussian elimination, eigenvalue computations and applications, iterative methods, linear programming, elements of convex optimisation including steepest descent, conjugate gradient, Newton’s and Gauss-Newton methods. Lagrange multi-
pliers and Kuhn-Tucker conditions.