This course is NOT running in academic year 2014-15
Please see CY902N
- To familiarise students with the techniques of Computational Linear Algebra.
- Understand the performance issue associated with Computational Linear Algebra.
- Familiarise students with standard Linear Algebra Libraries.
- Learn to formulate problems in terms of Linear Algebra operations.
- To introduce the central mathematical ideas behind algorithms for the numerical solution of Optimization problems.
- To provide theoretical justification for various algorithms and to outline basic issues of numerical analysis involved in Optimization problems.
- To present key methods for both constrained and unconstrained Optimization.
- To learn about available implementations and software packages.
- To learn about various heuristic strategies used in science and engineering applications, in particular for global Optimization.
- Use standard techniques of Computational Linear Algebra.
- Use standard software for Computational Linear Algebra.
- Formulate Scientific Computing problems as Linear Algebra Operations.
- Understand the Mathematical background to solving Numerical Optimization problems.
- Understand and use methods for constrained and unconstrained Optimization.
- Demonstrate familiarity with standard software package for Optimization.
- A good working knowledge of a scientific programming language (as for example taught in PX270 C Programming).
- A background in Linear Algebra (as taught in MA251).
- Some knowledge of Vector Calculus (as taught in MA231).
- Linear Algebra in Computational Science
- Computer Architecture and Linear Algebra
- Revision of Linear Algebra
- Basic Linear Algebra routines
- Small matrix eigenvalue problem and linear equations
- Large matrix eigenvalue problems and linear equations
- Singular Value Decomposition
- Programming techniques in Linear Algebra
- Mathematical Introduction to Optimization
- Optimality Conditions
- Unconstrained Optimization Algorithms
- Computational Issues
- Global Optimization
- Constrained Optimization Algorithms
- Optimal Control and Dynamic Programming
- More Computational Issues