Current Lecturer:

Current Course Homepage:

• 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.

1. Linear Algebra in Computational Science
   
2. Computer Architecture and Linear Algebra
   
3. Revision of Linear Algebra
   
4. Basic Linear Algebra routines
5. Small matrix eigenvalue problem and linear equations
   
6. Large matrix eigenvalue problems and linear equations
7. Singular Value Decomposition
8. Programming techniques in Linear Algebra
   
9. Mathematical Introduction to Optimization
10. Optimality Conditions
11. Unconstrained Optimization Algorithms
12. Computational Issues
13. Global Optimization
14. Constrained Optimization Algorithms
15. Optimal Control and Dynamic Programming
    
16. More Computational Issues