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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.