Applications of the generalized norm solver |

**Mandy Wong, Nader Moussa, and Mohammad Maysami**

The application of a L1/L2 regression solver, termed the generalized norm solver, to two test cases, shows that it is potentially an efficient method for L1 inversion and is easy to parameterize. The generalized norm solver iterates with conjugate direction. Our first test case, the line fitting problem, shows that the generalized solver is capable of removing outliers in data. Our second test case, the 1D Galilee problem, shows that the generalized solver can produce a satisfactory ``blocky" solution. In terms of parameters, a low threshold value, if giving convergent solution, gives the best result. Experience shows the optimal number of inner loop iterations is one.

- Introduction
- First test case: Linear fitting problem

- Test case two: The One Dimensional Galilee Problem
- Formulation of the 1D Galilee Problem
- Result of least-squares inversion of the 1D Galilee Problem
- Result of the 1D Galilee Problem using the Generalized norm solver

- Conclusion
- Acknowledgments
- Bibliography
- About this document ...

2009-10-19